agriculture – Page 36 – Nature Ecrogrow

From Water to Harvest: Exploring the Wonders of Hydroponic Agriculture

ABA is therefore necessary for the stomatal closure we observe in esb1-1. Te elevated ABA concentration we observe in leaves of esb1-1 compared to wild-type supports this conclusion. We also used the esb1-1sgn3-3 double mutant to test if SGN3 is involved in initiating this leaf ABA response. In leaves of the esb1-1sgn3-3 double mutant the elevated expression of a set of ABA signalling and response genes observed in esb1-1 is reduced to below that of wild-type . Further, the reduced stomatal aperture of esb1-1 is also recovered to wild-type levels in this double mutant . SGN3is therefore necessary for the ABA-dependent stomatal closure in response to the defective endodermal diffusion barrier in esb1-1. This raises the question of what links detection of a break in the endodermal diffusion barrier with ABA-driven closure of stomates in the leaf? Removal of endodermal suberin in esb1-1 expressing CDEF1 revealed a significant reduction in ABA-regulated gene expression, and a tendency to increasing stomatal aperture towards wild-type . Thus, increased suberin deposition in the endodermis of the esb1-1 root appears to play a partial role in the ABA controlled reduction in leaf transpiration. We have ruled out a role of local ABA signalling in controlling enhanced suberin deposition at the endodermis in esb1-1 . Using a similar strategy of expressing abi1 in the endodermis, in this case using the SCARECROW promoter , primarily active in the endodermis, we also show that in esb1-1 ABA signalling at the endodermis is not promoting stomatal closure or enhanced ABA signalling in leaves . We note that pSCR is also active in bundle sheath cell, and so ABA-signalling in these cells is also not involved in promoting stomatal closure in esb1-1. Furthermore,blueberry packaging containers enhanced ABA signalling in the endodermis is also not responsible for the initiation of the long-distance response of stomatal closure in leaves, and again it is more likely that suppression of ABA signalling is playing a role.

This can be seen in the fact that expression of abi1 in the endodermis, blocking ABA signalling, mimics the efect of esb1-1 on Lpr and stomatal aperture closure . However, these possibilities remain to be further explored. In contrast to these root-based or long-distance effects, the closure of stomata in leaves in response to a root-based CIFs/SGN3 derived signal is mediated by ABA locally in the leaves. We also note that the long distance signal connecting CIFs/SGN3 in roots with reduced leaf transpiration is currently unknown. Interestingly, a root-derived peptide has been recently identified as involved in long-distance signalling. In response to drought stress, CLE25 move from root to shoot and induces ABA accu-mulation in leaves and stomatal closure. Casparian strips have been suggested to play a critical role in forming a barrier to apoplastic diffusion to limit uncontrolled uptake and back fow of solutes from roots reviewed in . However, most Casparian strip mutants only appear to show fairly subtle phenotypic effects, and this has been a source of continued puzzlement. Here, we show that sensing damage to Casparian strips via leakage of the vasculature-derived CIF peptides from the stele into the cortex triggers a mechanism that inactivates aquaporins, promotes enhanced deposition of suberin limiting solute leakage in roots, and reduces transpiration in leaves, which all contribute to increasing solute concentration in the xylem . The overall outcome of this integrated response is a rebalancing of solute and water uptake and leakage. These physiological compensation mechanisms mitigate the loss of Casparian strip integrity, allowing relatively normal growth and development. A key part of this compensation mechanism is the ability of esb1-1 to limit water loss by the shoot by reducing stomatal aperture, in an ABA-dependent manner. This is clearly established by our observation that the esb1-1aba1 double mutant has severely reduced growth and seed production compared to either of the single mutants, and these growth defects can be partially supressed by the exogenous application of ABA .

The mechanisms we have identified are triggered by the loss of Casparian strips integrity. Such an event can occur during biotic stress including root nematodes infestation, and also during developmental processes such as lateral root emergence where Casparian strips are remodelled, suberin deposition occurs, and aquaporin expression is suppressed. Here, we describe novel outputs of the CIFs/SGN3 surveillance system that couple sensing of the integrity of the Casparian strip-based apoplastic diffusion barrier at the endodermis with pathways that regulate both solute leakage and hydraulic conductivity in the root . Long distance signals then connect these root-based responses with compensatory mechanisms in leaves which are mediated by local ABA signalling . Our dis-coveries provide a new framework which integrates our emerging understanding of the molecular development of the Casparian strip and suberin diffusion barriers with two of the major physiological functions required for plant survival – solute and water uptake.In recent years, California has tightened rules for reporting diversions of water for agriculture and other uses. One key challenge has been establishing workable standards for the collection of reliable data on relatively small and remote diversions — such as those for far-flung farms and ranches. Under new legislation, a certification program run by UC Cooperative Extension is helping to solve that problem. The State Water Resources Control Board views ac-curate diversion reporting as a key element of sound water management. “It’s incredibly important to monitor how much water comes into and goes out of the system,” says Kyle Ochenduszko, chief of water rights enforcement at the water board. Diversion reports are fed into a state database and support the orderly allocation of water resources by, for instance, enabling the board’s Division of Water Rights to inform water users when new requests to appropriate water might affect their own supply. Since 1966, the California Water Code has required diverters of surface water, with certain exceptions, to report their diversions to the water board. But in part because the water board lacked fining authority for many years, compliance was poor. In 2009, Senate Bill 8 gave the water board the authority to fine non-compliant diverters an initial $1,000, plus $500 for each additional day of failing to report.

Even so, SB 8 did not stipulate precisely how diversions were to be monitored. Rather, it required diverters to measure their diversions using the “best available technologies and best professional practices,” unless they could demonstrate that such technologies and practices were not locally cost-effective. That is, the requirement left wide latitude for interpretation. So things remained until 2015 — when Senate Bill 88 became law. This piece of legislation, passed amid a historically severe drought, directed the water board to draw up emergency regulations regarding water diversions. The regulations, once completed, required diverters of at least 100 acre-feet of water per year to hire an engineer or appropriately licensed contractor to install all monitoring devices. Now the requirements were clear. But the provision mandating installation by an engineer or contractor prompted an outcry from many smaller diverters, particularly those in remote areas of the state. For most diverters near sizable towns — Redding, say — complying with the regulations was manage-able, with expenses limited to the cost of a monitoring device and the services of an installer. But diverters in remote parts of Modoc County, for example, were looking at bigger bills, says Kirk Wilbur of the California Cattlemen’s Association. For such diverters, compliance might require importing an engineer or contractor from far away,blueberry packaging boxes which would entail significant travel expenses. If a site lacked electricity, as many do, the costs would pile higher . So how to reconcile the interests of the state’s diverters with those of the state? How best to balance the public and the private good? The answer, it turned out, was to empower diverters to install their own monitoring devices — with UCCE playing the empowering role. The idea originated with the Shasta County Cattlemen’s Association. It gained the support of the statewide Cattlemen’s Association. It took shape as proposed legislation in 2017 and was shepherded through the Legislature by Assemblyman Frank Bigelow . It breezed through both chambers with no votes in opposition — not even in committee. “All parties realized,” says Assemblyman Bigelow, “that Assembly Bill 589 would cut compliance costs and, as a result, increase compliance rates — which benefited both the regulators and the regulated community.” Essentially, AB 589 allows water diverters to in-stall their own monitoring devices if they successfully complete a monitoring workshop offered by UCCE. Further, it directed UCCE to develop the workshop in coordination with the water board. Khaled Bali, an irrigation water management specialist at the Kearney Agricultural Research and Extension Center, took the lead in drafting the coursework. “Then we met with the [water] board and got feedback,” Bali says. “We made changes until they said, ‘This looks good.’” Attendees at the workshops, which last three and a half hours, gain a solid foundation in the basic principles of diversion monitoring.

They learn how to monitor flows passing through a ditch, over a weir or through a pipe — or gathering in a pond. They learn how to build or install measuring devices appropriate for each type of diversion and how to calibrate those devices to comply with the state’s accuracy requirements. They learn how to navigate the water board’s rather detailed reporting system. Equipment for monitoring flows through open ditches might be limited to a tape measure, a timing device and a floating object. Installing a monitoring device for a diversion routed over a weir — a simple dam with an edge or notch that allows overflow — re-quires a bit more equipment. But once the installation is complete, the diverter need only read a staff gauge that shows the height of the water spilling over the weir’s crest . Diversions flowing through pipes must be outfitted with flow meters. Diversions feeding into a pond or reservoir can be monitored by tracking the depth of the water with a staff gauge, float or pressure transducer . So far, UCCE has offered the course in about 15 lo-cations, from Yreka to Bakersfield. According to Shasta County UCCE County Director Larry Forero — who teaches the $25 course along with Bali, Tehama County UCCE Advisor Allan Fulton and UC Davis–based UCCE Specialist Daniele Zaccaria — about 1,000 people had earned certificates of completion by early October. Even farmers and ranchers who divert less than 100 acre-feet per year are attending. “I’ve been floored,” says Wilbur, “by the number of diverters who have attended the course even though they aren’t required to — they want to better understand the regulations and make sure they’re doing the right thing.” It probably helps that the registration fee is a fraction of the cost of importing a faraway engineer. Due to their increasing use in a wide variety of beneficial industrial and consumer applications, ranging from use as a fuel catalyst, to chemical and mechanical planarization media, there have been increasing concerns about the potential environmental health and safety aspects of manufactured ceria nanomaterials.1,2 Ce is among the most abundant of the rare earth elements making up approximately 0.0046% of the Earth’s crust by weight .3 For example, Ce concentration in soils range from 2 to 150 mg kg−1 . 4 In Europe, the median concentrations of Ce were 48.2 mg kg−1 in soils, 66.6 mg kg−1 in sediment and 55 ng l−1 in water . There are many naturally occurring Ce containing minerals include rhabdophane, allanite, cerite, cerianite, samarskite, zircon, monazite and bastnasite.The existence of naturally occur-ring ceria nanoparticles is also likely and may play a key rolein controlling dissolved Ce concentrations,6 but precisely how the properties of naturally occurring ceria nanoparticles com-pare to manufactured ceria nanomaterials is unclear. There is concern that nanoceria, due to its small particle size and enhanced reactivity by design, may present unique hazards to ecological receptor species. Of critical importance are the redox properties of ceria which enables it to transition between CeĲIII and Ce, which are the key to understanding its potential toxicity.While there has been somewhat extensive investigation into the mammalian toxicity of ceria ,based on the present review, there has been considerably less effort invested into investigation of the environmental fate and effects of nanoceria. In this critical review, we discuss the likely points of environmental release along product life-cycles and resulting environmental exposure to nanoceria, methods of detection in the environment, fate and transport, as well as the available toxicity literature for ecological receptor species.

Each block has an agricultural office that is led by a Block Agricultural Officer

None of these studies consider whether engaging the supply side of the market can increase adoption by farmers. Survey data in agriculture suggest that input suppliers act as the second most popular source of information for Indian farmers. Informing private input suppliers about technological benefits is one way to take advantage of their intrinsic motivation to spread information. Yet, it has not been looked at as source of potential information agents with well-aligned incentives in promoting the adoption of new technologies. Our paper is the first to implement and test this as a new approach to doing agricultural extension.The rest of this paper is organized as follows. Section 2 gives more background information on the setting and outlines the experiment. Section 3 describes the data collection. Section 4 presents the main results on how targeting agricultural extension to input dealers increases technology adoption by farmers, particularly those with the highest potential benefit. Section 5 turns to analyzing a potential explanation for this result. Particularly, it focuses on whether dealers spread information to their customers and what motivates them to do so. Section 6 concludes. This section starts by providing background information on the standard methods used in agricultural extension. It also gives a description of how the public sector delivers information to farmers in our particular study area. We then outline the design of our main experiment to compare these standard methods with the more business-oriented approach of using agrodealers as information agents. Governments all over the world support agricultural extension services as a mode of information delivery. Ministries of agriculture typically have entire departments dedicated to providing these services. These departments oversee local administrative offices that hire front line extension agents whose role is to diffuse information about new agricultural technologies and practices to farmers.

The specific techniques used by agents vary across contexts, but the basic methods are largely consistent,indoor garden especially in poor countries. Agents usually work with selected “contact farmers” who are keen on trying new approaches and are ideally able to transmit knowledge to others in their social networks. They also organize farmer field days with cluster demonstration plots, where new seeds are grown by multiple farmers, to boost the diffusion of information. The public sector provides agricultural extension services for at least two reasons. First, markets do not exist for many new innovations. For instance, a new planting method may only be promoted by government agents because there is no scope for profiting from its sale in private markets. This contrasts with new seed varieties or material inputs that are produced and sometimes marketed by private firms. Second, many agricultural innovations are not developed, and hence not marketed, by private firms. Public entities such as national agricultural research systems or international research organizations frequently develop new seeds, inputs, and agricultural management practices. The public extension service then transmits information about these developments to farmers. In the context of our experiment, agricultural extension workers use many of these standard techniques. Each of the 10 districts in the sample is organized into blocks, where a block has an average of 135 villages.The BAO employs Assistant Agricultural Officers and Village Agricultural Workers who work in the field with farmers. Our sample consists of 72 blocks in 10 flood-prone districts of Odisha.We selected these areas because the technology being promoted — a flood-tolerant rice variety called Swarna-Sub1 — is most suitable for flood-affected areas.

The blocks in the sample represent around 20 percent of the blocks in the state. We randomly assigned 36 of these blocks to the treatment group where agrodealers were targeted to receive seeds and information. This randomization was stratified by district. The remaining 36 blocks serve as a comparison group where we supported the government extension service to carry out normal extension activities. Figure 1 displays the timeline of these interventions. Starting in May 2016 — about 6-8 weeks before planting time — we partnered with the government’s extension service to introduce Swarna-Sub1 into control blocks. We did this in a way that mirrors three common practices in agricultural extension. First, field staff provided 10 seed minikits of 5 kilograms each to the BAO, who then helped identify contact farmers to use the kits. The BAO chose 2 villages and 5 farmers in each village. Each kit contained only seeds for testing and some basic information about Swarna-Sub1. Our field staff then delivered the kits to the recommended farmers. Second, we provided another 150 kg of seeds to the BAO so that he could set up a cluster demonstration where the seeds would be used by several farmers on a contiguous set of plots. Based on seeding rates in the region, 150 kg allows for cultivation of 5-10 acres. The BAO chose where to do the demonstration and which farmers to target. Official government guidelines for organizing these clusters suggest that they be carried out in sites that are easily accessible to be viewed by many farmers. Moreover, sites should be representative of average conditions in the area. Third, we helped the BAO carry out a farmer field day in November — at the time right before harvest. The BAO selected the location of the field day and whom to invite. The purpose of the field day was for extension staff to train farmers about Swarna-Sub1, share information from the demonstrations, and hope that information will spread throughout the block. The objective of such an active control group is twofold. First, it ensures that each block is endowed with the same quantity of seeds. Therefore, the dealer-based treatment only differs on who received the new seeds and information. Second, the demonstrations and partnerships with contact farmers may not have taken place without our involvement. Forcing these activities to happen makes the treatment-control comparison more meaningful. Most importantly, it sets a higher bar for the dealer-based treatment by eliminating any possibility that the new technology would not be promoted by the government extension service.

Turning to the 36 treatment blocks, we obtained a list of 2,087 seed suppliers from the state Department of Agriculture. These include suppliers of two types: private seed dealers and Primary Agricultural Cooperative Societies . PACS are farmer groups that handle credit, seed supply, and procurement of output for farmers. We did not include them in the intervention because their incentives are not the same as those of private dealers. Seed sales are usually handled by a member that is not the residual claimant on any profits from the sale. Despite being fewer in number relative to PACS, private dealers account for almost 60 percent of the seeds sold to farmers. The sample consists of 666 private dealers, 327 of which were located in the treatment blocks. Armed with this list, our field staff entered each treatment block and located five dealers interested in receiving seed minikits and an informational pamphlet about SwarnaSub1. In some blocks fewer than 5 dealers were available. We provided additional seed to each dealer in these cases to guarantee that a full 200 kilograms were introduced. The list provided by the Department of Agriculture did not have enough locatable dealers in some cases. In these circumstances, our field staff provided the seeds to other local agrodealers.Overall, seeds and information were provided to 151 dealers across the 36 treatment blocks.119 of these were from the original list. Once provided with seeds and information, the dealers were left alone to decide how to use them. We asked dealers about their intended uses. They overwhelmingly stated that they would use the seeds for testing on their own farms and would provide them to good customers for testing.Our intervention did not include any additional assistance to dealers. This differs from standard methods in agricultural extension where agents continually revisit their contact farmers. We allowed dealers to learn on their own because in theory they should be motivated to learn about a new product that could enhance their business. The goal of our treatment is to measure whether this motivation causes information to flow to farmers and ultimately increases adoption. Not intervening further ensures that our treatment effect is driven by any real-world incentives dealers have to learn, rather than heavy monitoring by our partners. Dealers in our sample are small business entrepreneurs. Some operate out of their homes, while others maintain shops in rural towns. 44 percent of dealers sell only seeds,hydroponic farming with fertilizers and pesticides being the most common inputs sold by the other dealers. Dealers are highly local. The median dealer sells enough rice seed to cover roughly 400 acres, which amounts to the rice area cultivated by 150 farmers.Importantly, dealers tend to serve the same customers from year to year. Another important feature of our context is that 84% of the seeds sold by dealers in our sample are produced by the state-run Odisha State Seed Corporation . Government subsidies explain this. Seeds produced by the state are subsidized at a rate of approximately 40 percent. No subsidies exist for seeds produced by private companies. As licensed agents, dealers receive a fixed commission that amounts to about 8 percent of the pre-subsidy price. All varieties have the same final price for farmers. Thus, the margin for dealers is the same across all types of varieties. Hence dealers have no direct financial incentive to sell one variety over another. Turning to the second season , we ran an SMS messaging experiment to compare our intervention with this “lighter touch” information treatment.

The random delivery of SMS messages allows us to test whether our dealer treatment substitutes basic knowledge that can be easily transmitted via ICT technology. Furthermore, it allows us to compare the direct effects of the two approaches. The messaging was simple. It informed farmers that Swarna-Sub1 is a new variety that is suitable for medium-low land in terms of elevation, matures in 145 days, and can tolerate up to two weeks of flooding. The message also stated that it was being produced by OSSC and could be available at local dealers. As a sampling frame, we obtained mobile numbers for 75,616 farmers that had registered for the state government’s Direct Benefit Transfer scheme to obtain seed subsidies.These farmers are located across the 261 gram panchayats that cover our main estimation sample, as outlined below. The SMS treatment was randomized at the gram panchayat level, resulting in messages being delivered to 37,783 of the names on the list.We anticipated that dealers and contact farmers would use the demonstration minikits for learning in 2016 and any possible treatment effects could first be detected during year two . Our main followup survey therefore took place in August-September 2017 — around 15 months after the interventions. Its purpose was to measure adoption of seed varieties by rice farmers. To minimize measurement error, we timed the survey to be right after planting. Our sample consists of 7,200 farmers. These farmers were drawn from a random sample of 261 gram panchayats — an administrative unit usually consisting of around 8 villages.Before drawing this sample, we excluded gram panchayats that had any village within 1.5 kilometers of the block boundary.We removed these areas to reduce any interference caused by farmers possibly obtaining seeds from other blocks. The 261 sample gram panchayats had 75,616 farmers registered for the DBT program for seed subsidies. Using this database as a sampling frame, we randomly drew 100 farmers from each block . These farmers are spread across 1,333 villages. Figure A1 shows their geographic dispersion across the 10 districts in the experiment. Survey teams succeeded in locating and surveying 6,653 of the farmers. Of these, 93 percent were currently cultivating rice. Table A1 shows no significant differences in the probabilities of being surveyed or growing rice between treatment and control groups. The survey focused on which seed varieties were currently being used for rice cultivation. Surveyors went through a list of 30 varieties and asked farmers which ones they were currently using and the amount of land being grown.In addition to these adoption data, we obtained information on contacts with agricultural extension agents during the last year, topics discussed during these conversations, whether the farmer had seen any seed demonstrations, and whether they had recently learned about Swarna-Sub1.

Agriculture plays a central role in many important and influential hypotheses about human history

Greater effective soil age in little-eroded uplands soils means a longer time for the accumulation of recalcitrant organic matter in older soils—and probably more importantly, it allows the accumulation of noncrystalline minerals that stabilize soil organic matter.We further suggest that the patterns for total P are more complex because its pools reflect both weathering and loss and retention by both organic matter and mineral adsorption, which are greater in the upland slope positions.Overall, these results illustrate that erosion and deposition have a rejuvenating effect on the supply of rock-derived nutrients in these valley landscapes —one that suffices to make both lower slope and alluvial soils fertile enough to support intensive pre-contact agricultural systems in both valleys despite the infertility of the upland soils surrounding them.However,differences in the structures of the valleys influenced their ability to support intensive agriculture prior to European contact.Halawa Valley and other large valleys on older islands have well-developed colluvial aprons surrounding their alluvial floors.In contrast, Pololu¯ Valley lacked the potential for lower-slope rainfed agriculture because the high subsidence rate of Hawai’i Island causes a sharp transition between slopes too steep to cultivate and the nearly flat valley floor —a process that is accentuated by the rapid glacial-melt-driven sea level rise of the past approximately 20 ky.Other major valleys on Kohala Volcano have similar structures—including the largest, Waipi’o Valley, which was a major center of precontact Hawaiian settlement.Considering only the area bounded by the cliff tops on the valley sides and waterfalls at the head of the valleys,flood and drain table differences in subsidence rates and corresponding in-fill histories cause large differences in the distribution of slopes suitable for agriculture within Pololu¯ and Halawa.

Assuming that slopes of less than 5 could have been made suitable for intensive pond field systems, 17% of the 423 ha surface of Pololu¯ Valley could support pond fields ; only 6% of the 692 ha surface of Halawa Valley had slopes less than 5.Further, assuming that 12 represents an upper threshold for intensive rainfed agriculture, 16% of Halawa Valley has slopes between 5 and 12 , as opposed to only 5% of Pololu¯ Valley.Available archaeological evidence for pre-contact agricultural systems in Pololu¯ and Halawa is consistent with our findings on valley topography and soil fertility.Tuggle and Tomonari-Tuggle found evidence for both irrigated and rainfed fields on the flat alluvial floor of Pololu¯.They attribute the fact that not all the Pololu¯ alluvium was irrigated to the valley’s hydrologic conditions; the valley floor is so large relative to its watershed area that stream flow was inadequate to have watered the entire valley floor.In Halawa Valley, the entire area of alluvium was converted to irrigated pond fields, which also extended onto the lower colluvial slopes.More importantly, well-defined rainfed cultivation plots with stone-faced terraces and walls extend well up the colluvial slopes in Halawa, encompassing an area greater than the total area of irrigated pond fields there.Rosendahl mapped the Kapana area of Halawa Valley, providing a detailed example of intensive rainfed agricultural terraces, integrated with habitation sites and small temples.Significantly, mid-nineteenth century land records from Halawa demonstrate that most claimants included both irrigated as well as rainfed areas in their claims , showing that the two kinds of agriculture were integral parts of the overall production system at the household level.The broader implications of this potential for intensive rainfed agriculture on colluvial slopes of the valleys on the older islands in the Hawaiian Archipelago are substantial.Analyses of the distribution of intensive agricultural systems and their consequences for the dynamics of Hawaiian society have considered irrigated and rainfed systems to have been spatially separated, due to the very different ecosystem and landscape properties that favor their development.

Because these types of agricultural systems differ both in their ability to produce a surplus over agricultural labor and in their vulnerability to drought—with both comparisons favoring the irrigated pond field systems—these contrasting systems could have contributed to the development of rather different societies, in areas or on islands dominated by one system or the other.The islands of Hawai’i and to a lesser extent Maui were based largely upon intensive rainfed systems, with only a few well-watered irrigated valleys.In contrast, the older islands in the archipelago have been thought to be based mostly upon irrigated pond field systems.However, the evidence here suggests that the older islands likely maintained integrated pond- field/rainfed systems and that, as in Halawa Valley, the peripheral rainfed systems could have covered a larger area than did irrigated pond fields.A similar pattern has been suggested in the leeward Makaha Valley of O’ahu, where archaeological survey confirmed the presence of extensive areas of dryland gardening on colluvial slopes, but where irrigation was confined to smaller areas in the valley interior.The potential for developing integrated pond- field/rainfed systems on colluvial slopes on the older islands strengthens the contrast between the agricultural production potential of Hawai’i Island versus the older islands.It has been suggested that pressures to maintain surplus production in rainfed, drought-prone agricultural areas could have driven the elites of Hawai’i Island towards marriage alliances with elites of the older islands, and/or towards conquest of those islands —and the development of integrated pond field/rainfed systems on the older islands would only have increased their attractiveness as potential acquisitions.Moreover, integrated systems on the older islands could have boosted their potential agricultural yields, and the diversity of foods they could produce, to levels approaching the total productivity of the much larger island of Hawai’i.These dynamics should be incorporated into our understanding of the dynamics of Hawaiian society, and those of other indigenous societies in which similar dynamics could occur.For the vast majority of our evolutionary history, humans subsisted by hunting animals and gathering plants.

Around 12,000 years ago, we began to take a more direct role in the process of food production, domesticating animals and cultivating crops in order to meet our nutritional requirements.This subsistence revolution is thought to have occurred independently in a limited number of places.This new way of life is arguably the most important process in human history, and its dramatic consequences have set the scene for the world we live in today.Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution, yet we often lack systematic information about the productivity of past agricultural systems on a scale large enough to test these theories properly.Here, we outline how explicit crop yield models can be combined with high quality historical and archaeological information about past societies in order to infer how agricultural productivity and potential have changed temporally and geographically.The paper has the following structure: First, we introduce the ways in which agriculture is involved in theories about human social evolution, and stress the need to scientifically test between competing hypotheses.Second, we outline what information we need to model about past agricultural systems and how potential agricultural productivity and carrying capacity can provide a useful way of comparing societies in different regions and time periods.Third, we discuss the need for a systematic, comparative framework for collecting data about past societies.We introduce a new databank initiative we have developed for collating the best available historical and archaeological evidence.We discuss the kinds of coded information we are collecting about agricultural techniques and practices in order to inform our modelling efforts.We illustrate this task by presenting three short case studies summarizing what is known about agricultural systems in three different regions at various time periods.We discuss the challenges confronting this approach, and the various limitations and caveats that apply to the task at hand.Fourth, we outline how we can combine a statistical approach of modelling past crop productivity based on climate inputs with the kind of historical information we are collecting.The development of agriculture and the ways it has spread and intensified are fundamental to our understanding of the human past.For example, authors such as Renfrew, Bellwood, and Diamond argue that early agricultural societies enjoyed a demographic advantage over hunter-gatherers, which fueled a series of population expansions resulting in agriculturalists spreading out to cover much of the world, taking their culture and languages along with them.At the beginning of the European age of exploration, agricultural societies had pushed the distribution of forager populations in the Old World to only those places that were marginal for agriculture.

Widespread forager populations were present in the Americas and Australia, but these too eventually gave way to agricultural populations of European origin.Agriculture raised the carrying capacity of the regions in which it developed and spread,rolling bench leading to people living at higher densities with a more sedentary way-of-life than was previously possible.However, the development of agriculture did not stop there.Further improvements in agricultural technologies and techniques, and processes such as artificial selection further raised the productivity of agriculture and the size of the population that could be supported in any one region.These improvements ultimately enabled humans to live in large urban conglomerations with extremely high population densities.Influential models of agricultural innovation, starting with the work of Esther Boserup , argue that advances occur in response to increases in population, and the subsequent decreasing availability of land.This drives farmers to invest more labor in producing food.In other words, there is feedback in the system that leads to the increasing intensification of agriculture.These processes of intensification, whatever their cause, can occur in a number of different ways and have had important consequences.From the fields and hedgerows of Northern Europe to the mountainside rice terraces of the Ifugao of the Philippines , through to the deforested slopes of Easter Island , agricultural populations have dramatically altered the landscapes around them.Agriculture is central to many theories about how larger-scale complex societies evolved.Under functionalist views of social complexity more productive agricultural systems allowed for ‘surplus’ production, and enabled a more extensive division of labor.This surplus production allowed for individuals who did not grow their own food, enabling the creation of specialized managers and rulers, and occupational artists and artisans.It is argued that this division of labor increases efficiency and coordination, enabling more complex societies to out-compete less complex societies either directly or indirectly.Under this view, not only is a rich resource base a necessary condition for the emergence of complex societies, but it is also a sufficient one.If this is correct, it follows that differences in agricultural productivity can explain why some regions developed more complex societies than others.Changes in agricultural intensity have also been linked to changes in the ritual and religious life of human groups.It is argued that hunter-gathers and early agriculturalists, who lived in small groups and faced high risks from hunting of large animals, tended to participate in dysphoric, “imagistic” rituals that, although rarely experienced, are typically emotionally intense.Such rituals act as a mechanism for creating social cohesion via ‘identity fusion’.A greater dependence on agriculture led to increased group sizes, and required different forms of cooperation and coordination in order to successfully produce food.New ritual forms developed that were organized around daily or weekly cycles but with less intense emotional experiences.It is argued that this ‘routinization’ enabled strangers to recognize and identify with others as members of a common in-group, enabling trust and cooperation on a hitherto unknown scale.It is clear that agriculture is of fundamental importance to studies of the human past.The ideas outlined above represent just a flavor of the ways agriculture and agricultural productivity enter into our understanding of the long-term patterns and processes of human history.

Importantly, these ideas are hypotheses that require testing against other plausible narratives.For example, it has been argued that an important factor driving the evolution of complex societies was intensive forms of conflict between nomadic pastoralists and settled agrarian societies that selected for increasingly larger and more cohesive societies.Thus, complex societies tended to emerge on the border of the Eurasian Steppe and spread out from there.Under this view, agriculture is seen as necessary but not sufficient to explain the observed variation about where and when such societies developed.When attempting to understand the past we should seek to test between competing hypotheses, rather than simply focusing on a single favored idea.In order to do this, it is important to have relevant data on past agricultural systems and their productivity and potential.These systems exhibit a great deal of variation, and are of varying levels of intensity.To enable more direct comparisons across different regions and time periods, it will be important to have explicit models that translate different agricultural systems across space and time into a common currency.This will allow us to perform statistical analyses so that we can directly test alternative hypotheses.

Some specimens could only be identified to the family level

Since its colonial introduction to the Old World, the golden berry also has been referred to as the cape gooseberry; however, Physalis peruviana from South America is marketed in the United States most commonly as golden berry and sometimes Picchu berry, named after Machu Picchu in order to associate the fruit with its origin in Peru and to address the fact that this fruit is not actually a gooseberry as the name cape gooseberry implies. As a member of the plant family Solanaceae, it is closely related to the tomatillo . High in Vitamins A, B, and C, as well as phosphorus and protein, golden berries also have a range of documented medicinal uses, including antitussive, antihelmintic, antidiabetic, and diuretic properites; they are also used to combat a range of maladies from eczema to conjunctivitis to gonorrhea . Recent studies have discovered 14 new compounds in various species of wild tomatillo that have anti-cancer properties; these compounds, known as withanolides, are already showing promise in combating a number of different cancers and tumors without noticeable side effects or toxicity . Passion fruit/maracuyáis a woody perennial climbing vine that originated in Brazil and then spread throughout South America. Cultivated in humid and dry climates, passion fruits can be grown up to 1,500 masl, but require non-flooded land with good drainage to produce successfully. Both the fruit pulp and seeds of this sweet fruit are consumed as desserts, and the fruits are also squeezed into juices and made into salsas. Similar to cotton, passion fruits can be pressed for oil, which is used to aid digestion. Passion fruits also possess magical and medicinal properties; they are used an as anaphrodisiac , as well as a muscle relaxer and sedative . Cactus fruits of the genus Opuntia are abundant in the Moche Valley today; this plant grows between 500 and 3,000 masl in interandean valleys and survives in soil with low to medium soil fertility. The pulp of the cactus fruit is consumed also has a variety of other uses, including medicinal ; cosmetic ; to attract cochineal insects used for dyes; and as fodder for livestock .

In addition, various wild plum or wild cherry/cerezospecies are distributed throughout Peru, wild and cultivated up to 3,500 masl, with known comestible and medicinal uses .A number of other miscellaneous/wild taxa were identified in the assemblages,vertical grow rack including various weedy taxa found in agricultural fields and on habitation sites, many of which have known economic uses . Others likely represent incidental inclusions, unintentionally transported to the site in the clothing of family members and fur of livestock returning from agricultural fields. In contrast to field cultigens and tree crops that produce large seeds or rind fragments, many of the miscellaneous/wild species discussed below have not received much treatment in the Andean archaeological literature. Only in the past few decades have paleoethnobotanists made attempts to systematically identify small weedy seeds from archaeological samples , in contrast to the recovery of larger taxa hand-picked during excavation or from larger mesh/screen sizes that characterize earlier excavation techniques.Some of these families are represented by multiple genera and hundreds of species, so it is difficult to make specific inferences about their economic uses by Moche Valley residents. Some of these families are well adapted to disturbed environments and occupy agricultural fields , in open uncultivated areas , or on rocky hill slopes or other relatively undisturbed areas . Other species identified to the species or genus level have well-documented economic uses, with data from ethnographic studies and some have longer histories of use evidenced archaeologically. Many of the taxa discussed below had multiple uses, including as food, medicine, fodder, fuel, or other purposes, with different portions of plants used for different purposes, including with different preparation methods . I draw primarily on ethnobotanical uses discussed by Brack Egg , along with other scholars cited below. Food taxa in the miscellaneous/wild category include amaranth/kiwicha 17, lupine/tarwi , mesquite/algorrobo , plantain/Plantago spp., oregano , purslane/verdolaga , rattlepod/crotalaria , saltbush/orache , sow thistle , trianthema , vetch/haba , wildbean and a member of the genus Rubus. Some of these comestibles are fairly well known; for example, amaranth is fairly cosmopolitan in cuisine, as a nutritious grain that can be toasted, popped, ground into flour, or boiled for gruel . Native to Peru, amaranth is distributed throughout the Andes from Colombia to Argentina, on the on the coast, highlands , and high jungle.

Both wild and cultivated , different species of amaranths grow within different elevation zones, with coastal varieties that can be grown up to 500 masl and altiplano varieties up to 4000 masl . Brack Egg lists two wild species that can be grown in the north coast region . Amaranth has long been used as a food source in the Andes, including by the Inka , with archaeological evidence of cultivation going back as far as 2,000 years, recovered in tombs in northwestern Argentina . It is also used as livestock fodder and has medicinal uses, including to treat diarrhea, sore throats, menstrual cramps, and rashes. The green leaves also be can be eaten like vegetables . Mesquite, or algarrobo , is another well-known food; ripened seed-pods are often ground into flour and also used to make chicha. The seed pods also serve as camelid fodder. The sweet, molasses-like flavor of mesquite is incorporated into many beverages in Peru today, including algarrobina, a cocktail that uses mesquite syrup extract. Thriving in alluvial and rocky soils up to 1,500 masl, mesquite trees grow quickly and are long-lived . Their hardwoods are a source of long-burning firewood and charcoal as well as a raw material for wooden tools . The leaves, greens, and seeds of many of the miscellaneous/wild taxa may have been eaten raw or cooked, including lupine, plantain, purslane, saltbush, rattlepod, Rubus spp., sow thistle, vetch, and wildbean, while others were used as seasoning or condiments, such as oregano or trianthema . Some of these taxa have moderate to high degrees of toxicity and must be processed, e.g., lupine, which has a high alkaloid content. A member of the Fabaceae family, lupine, or tarwi, is typically considered to be a ‘highland’ food, as it grows up to 3,850 masl . A number of the miscellaneous/wild taxa have known medicinal uses as well, including acacia/faique , amaranth, knotweed/smartweed , milk thistle/cardo , oregano, purslane, ragweed/ambrosía , rattlepod, saltbush, sedge/piri-piri , spurge , tillandsia/achupalla , sage/salvia , shoreline purslane/capin , sida/pichana , vervain/verbenaand violet/violeta .

These plants have known analgesic properties and been documented for the their use in treating a range of maladies, from coughs/colds, headaches/earaches/throat aches, gastrointestinal distress, rashes, and menstrual cramps, among others, and also have been used in fertility management as contraceptives or abortive agents . Certain taxa, e.g., vervain, have known uses in veterinary medicine as well; used to treat cattle hooves in the Andes today , it is possible that vervain could have been used to treat prehistoric ungulates . Certain spurges that have known purgative properties, along with sedges that have aphrodisiac properties have documented uses in shamanic rituals as well . Some of the miscellaneous/wild taxa also have known fuel uses, including tillandsia, saltbush, mesquite, and acacia. A few archaeological studies have identified plant taxa and other organic materials including woods and other herbaceous plants used as prehistoric fuels on the north coast , for cooking, firing ceramics, and working metal. In Inka times, fuel was an important tribute item . Beyond potential inventories of north coast fuels, the social relations associated with fuel use remain poorly understood. Moche Valley residents likely burned dung as a source of fuel in addition to grasses and tree fuels . In order to identify dung burning archaeologically, Wright suggests that researchers consider the following: if there is a basis for using dung such as a shortage of available wood, the presence of suitable dung-producing animals in the archaeological context considered, recognizable animal dung in the archaeological deposits,vertical grow table and the recovery of such samples from hearth contexts . No wood analysis was conducted in this dissertation, so it is difficult to say at this point if there was a shortage of any particular taxa in the Moche Valley that would have been used for fuel. As discussed further below, seeds of the potential fuel taxa only were recovered in small quantities, but future wood charcoal analyses may reveal a different pattern. The Moche Valley does not have the dense stands of algarrobo trees witnessed in the more northerly Jequetepeque Valley ; I imagine that Moche Valley residents likely used a combination of gathered wild plant taxa and dung as fuel sources.

Camelids would have served as suitable dung-producing animals; indeed, ample amounts of dung, from camelids as well as guinea pigs, or cuy , were recovered throughout the Moche Origins Project excavations at MV-224, MV-225, and MV-83, and was present in many flotation samples . Hastorf and Wright and Miller and Smart argue that animal dung can serve as a vector for seeds from fodder plants, e.g., Poaceae, Chenopodiaceae, Verbenaceae, and Boraginaceae, taxa that were present in the Moche Valley assemblages. A number of the miscellaneous/wild taxa were likely used for animal fodder as well, including amaranth, grasses including crown grass/gramaloteand panic grass/grama , lupine, rattlepod, sandbur/pega pega , sida, tillandsia, trianthema, vetch, and wildbean. All of these taxa have ethnographically documented cases of fodder use for livestock . Brack Egg lists sida in particular as a fodder used for guinea pigs. However, as Wright identifies, separating taxa used for fodder from taxa used for human consumption is complicated. Fodder can often be the same species as food used for human consumption and may also be processed and stored in a similar fashion . Ethnographic data suggest that the boundary between food and fodder is flexible and often depends upon the success of the harvest. In other words, what might be fodder in one year, could be used for human consumption the next year if yields of more preferred foods are low. This distinction even relates to fodder and fuel; for example, the preferred economic use of tillandsia is as fuel, but it can also serve as a fallback fodder for animals . Finally, some of the miscellaneous/wild taxa have other technological uses, as construction materials, for matting/thatching , textile production, etc. Sage and field madder have documented uses as green/yellow or red dyes, respectively . Other taxa may simply be the result of incidental inclusions in the archaeobotanical assemblages, and may not have been used by Moche Valley residents. The archaeobotanical assemblages from the five Moche Valley sites include a combination of wild and cultivated plants, with ecological requirements in many cases involving anthropogenic intervention. Moche Valley farmers had sustained access to water from irrigation canals, resulting in the creation of a landscape of cultivated fields, orchards, and fallow pastures.

Aside from a wide range of field cultigens and tree crops , other fruits would have been actively managed, likely lining fields. A number of miscellaneous wild species thrive in areas disturbed by humans and likely existed and were harvested in gardens even if not intentionally grown. Certain economic weedy species thrive along irrigation canals ; in disturbed areas ; and in fields under cultivation or recently fallowed , presenting Moche Valley farmers with opportunities to collect them while managing farming tasks. Ethnographic and Ethnohistoric Perspectives of Food Preparation and Processing Some materials and techniques of processing and preparation of plant foods recorded in ethnohistoric documents and witnessed today may have some bearing on past practices. Many of the edible plants and animals listed in the inventories of prehistoric sites in Peru are still grown, purchased, or gathered today, and while I do not assume an unbroken continuity for two millennia regarding the ways in which foods were processed and prepared, ethnographic and ethnohistoric sources are a useful starting point for thinking about the organization of food ways. Throughout South America, the practices of baking in ovens or frying over fires were virtually unknown in prehispanic times . While much literature has focused on Inka or highland traditions rather than coastal valleys, a small amount of ethnographic and ethnohistoric information is available for the north coast region.

Well-curated GGB databases play an important role in the data lifecycle by facilitating dissemination and reuse

The AgBioData consortium was formed in 2015 in response to the need for GGB personnel to work together to come up with better, more efficient database solutions. The mission of the consortium, comprised of members responsible for over 25 GGB databases and allied resources, is to work together to identify ways to consolidate and standardize common GGB database operations to create database products with more interoperability. FAIR principles have rapidly become standard guidelines for proper data management, as they outline a road map to maximize data reuse across repositories. However, more specific guidelines on how to implement FAIR principles for agricultural GGB data are needed to assist and streamline implementation across GGB databases. The results were used to focus and foster the workshop discussions. Here we present the current challenges facing GGBs in each of these seven areas and recommendations for best practices, incorporating discussions from the Salt Lake City meeting and results of the survey. The purpose of this paper is 3-fold: first, to document the current challenges and opportunities of GGB databases and online resources regarding the collection, integration and provision of data in a standardized way; second, to outline a set of standards and best practices for GGB databases and their curators; and third, to inform policy and decision makers in the federal government, funding agencies, scientific publishers and academic institutions about the growing importance of scientific data curation and management to the research community. The paper is organized by the seven topics discussed at the Salt Lake City workshop. For each topic, we provide an overview, challenges and opportunities and recommendations. The acronym ‘API’ appears frequently in this paper, referring to the means by which software components communicate with each other: i.e. a set of instructions and data transfer protocols.

We envision this paper will be helpful to scientists in the GGB database community, publishers, funders and policy makers and agricultural scientists who want to broaden their understanding of FAIR data practices.Biocurators strive to present an accessible,ebb flow tray accurate and comprehensive representation of biological knowledge . Biocuration is the process of selecting and integrating biological knowledge, data and metadata within a structured database so that it can be accessible, understandable and reusable by the research community. Data and metadata are taken from peer-reviewed publications and other sources and integrated with other data to deliver a value-added product to the public for further research. Biocuration is a multidisciplinary effort that involves subject area experts, software developers, bio-informaticians and researchers. The curation process usually includes a mixture of manual, semi-automated and fully automated workflows. Manual biocuration is the process of an expert reading one or several related publications, assessing and/or validating the quality of the data and entering data manually into a database using curation tools, or by providing spreadsheets to the database manager. It also encompasses the curation of facts or knowledge, in addition to raw data; for example, the role a gene plays in a particular pathway. These data include information on genes, proteins, DNA or RNA sequences, pathways, mutant and nonmutant phenotypes, mutant interactions, qualitative and quantitative traits, genetic variation, diversity and population data, genetic stocks, genetic maps, chromosomal information, genetic markers and any other information from the publication that the curator deems valuable to the database consumers. Manual curation includes determining and attaching appropriate ontology and metadata annotations to data. This sometimes requires interaction with authors to ensure data is represented correctly and completely, and indeed to ask where the data resides if they are not linked to a publication. In well-funded large GGB databases, manually curated data may be reviewed by one, two or even three additional curators.

Manual biocuration is perhaps the best way to curate data, but no GGB database has enough resources to curate all data manually. Moreover, the number of papers produced by each research community continues to grow rapidly. Thus, semi-automated and fully automated workflows are also used by most databases. For example, a species-specific database may want to retrieve all Gene Ontology annotations for genes and proteins for their species from a multi-species database like UniProt . In this case, a script might be written and used to retrieve that data ‘en masse’. Prediction of gene homologs, orthologs and function can also be automated. Some of these standard automated processes require intervention at defined points from expert scientist to choose appropriate references, cut off values, perform verifications and do quality checks. All biocuration aims to add value to data. Harvesting biological data from published literature, linking it to existing data and adding it to a database enables researchers to access the integrated data and use it to advance scientific knowledge. The manual biocuration of genes, proteins and pathways in one or more species often leads to the development of algorithms and software tools that have wider applications and contribute to automated curation processes. For example, The Arabidopsis Information Resource has been manually adding GO annotations to thousands of Arabidopsis genes from the literature since 1999. This manual GO annotation is now the gold standard reference set for all other plant GO annotations and is used for inferring gene function of related sequences in all other plant species . Another example is the manually curated metabolic pathways in Ecocyc, MetaCyc and PlantCyc, which have been used to predict genome-scale metabolic networks for several species based on gene sequence similarity . The recently developed Plant Reactome database has further streamlined the process of orthology-based projections of plant pathways by creating simultaneous projections for 74 species. These projections are routinely updated along with the curated pathways from the Reactome reference species Oryza sativa . Without manual biocuration of experimental data from Arabidopsis, rice and other model organisms, the plant community would not have the powerful gene function prediction workflows we have today, nor would the development of the wide array of existing genomic resources and automated protocols have been possible. Biocurators continue to provide feedback to improve automated pipelines for prediction workflows and help to streamline data sets for their communities and/or add a value to the primary data.

All biocuration is time consuming and requires assistance from expert biologists. Current efforts in machine learning and automated text mining to pull data or to rank journal articles for curation more effectively work to some extent, but so far these approaches are not able to synthesize a clear narrative and thus cannot yet replace biocurators. The manual curation of literature, genes, proteins, pathways etc. by expert biologists remains the gold standard used for developing and testing text mining tools and other automated workflows. We expect that although text-mining tools will help biocurators achieve higher efficiency, biocurators will remain indispensable to ensure accuracy and relevance of biological data. GGB databases can increase researchers’ efficiency, increase the return on research funding investment by maximizing reuse and provide use metrics for those who desire to quantify research impact. We anticipate that the demand for biocurators will increase as the tsunami of ‘big data’ continues. Despite the fact that the actual cost of data curation is estimated to be less than 0.1% of the cost of the research that generated primary data , data curation remains underfunded .Databases are focused on serving the varied needs of their stakeholders. Because of this, different GGB databases may curate different data types or curate similar data types to varying depths, and are likely to be duplicating efforts to streamline curation. In addition, limited resources for most GGB databases often prevent timely curation of the rapidly growing data in publications.The size and the complexity of biological data resulting from recent technological advances require the data to be stored in computable or standardized form for efficient integration and retrieval. Use of ontologies to annotate data is important for integrating disparate data sets. Ontologies are structured, controlled vocabularies that represent specific knowledge domains . Examples include the GO for attributes of gene products such as subcellular localization, molecular function or biological role,flood and drain tray and Plant Ontology for plant attributes such as developmental stages or anatomical parts. When data are associated with appropriate ontology terms, data interoperability, retrieval and transfer are more effective. In this section, we review the challenges and opportunities in the use of ontologies and provide a set of recommendations for data curation with ontologies.To identify current status and challenges in ontology use, an online survey was offered to AgBioData members. The survey results for ontology use in databases for each data type are provided in Table 1 and a summary of other survey questions such as barriers to using ontologies are provided in the supplementary material 1. In addition, the ways ontologies are used in data descriptions in some GGB databases are described in supplementary material 2. To facilitate the adoption of ontologies by GGB databases, we describe the challenges identified by the survey along with some opportunities to meet these challenges, including a review of currently available ontologies for agriculture, ontology libraries and registries and tools for working with ontologies.

A key component of FAIR data principles is that data can be found, read and interpreted using computers. APIs and other mechanisms for providing machine-readable data allow researchers to discover data, facilitate the movement of data among different databases and analysis platforms and when coupled with good practices in curation, ontologies and metadata are fundamental to building a web of interconnected data covering the full scope of agricultural research. Without programmatic access to data, the goals laid out in the introduction to this paper cannot be reached because it is simply not possible to store all data in one place, nor is it feasible to work across a distributed environment without computerized support. After a brief description of the current state of data access technology across GGB databases and other online resources, we more fully describe the need for programmatic data access under Challenges and Opportunities and end with recommendations for best practices. Sharing among AgBioData databases is already widespread, either through programmatic access or other means. The results of the AgBioData survey of its members indicate that GGB databases and resources vary in how they acquire and serve their data, particularly to other databases. All but 3 out of 32 GGB databases share data with other databases, and all but two have imported data from other database. Some make use of platforms, such as Inter Mine , Ensembl and Tripal , to provide programmatic access to data that is standard within, but not across the different options. Other databases develop their own programmatic access or use methods such as file transfer protocol . Finally, some databases provide no programmatic access to data. A number of infrastructure projects already exist that support AgBioData data access needs, most of which have been adopted to some degree by different GGB platforms . A more recent approach to facilitate data search, access and exchange is to define a common API that is supported by multiple database platforms. An example of this is BrAPI , which defines querying methods and data exchange formats without requiring any specific database implementation. Each database is free to choose an existing implementation or to develop its own. However, BrAPI’s utility is restricted to specific types of data. Alternatively, the Agave API provides a set of services that can be used to access, analyse and manage any type of data from registered systems, but is not customized to work with GGB databases.Aside from primary repositories like GenBank, model organism and specialty databases remain the primary means of serving data to researchers, particularly for curated or otherwise processed data. These databases represent different community interests, funding sources and data types. They have grown in an ad hoc fashion and distribute data in multiple formats, which are often unique to each database and are may be without programmatic access. Below, we lay out some of the challenges and opportunities in programmatic data access faced by GGB researchers using the current landscape of databases. Exploration of these use cases yielded a set of common data access requirements under five different themes, summarized in Table 7.Large comparative genomic portals exist but have limitations in their utility for specialized communities, such as not incorporating data from minor crop species or crop wild relatives or rarely handling multiple genomes for the same species.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Fertilizers are generally considered risk-increasing inputs

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

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

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

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

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