Tag: agriculture

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?

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.

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

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

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

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

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

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

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

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

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

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

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