hydroponic growing – Page 55

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

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

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

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

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

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

Fertilizers are generally considered risk-increasing inputs

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

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

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

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

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

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

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

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

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

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

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

Articles studied either one or various arthropodrelated ES and EDS

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

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

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

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

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

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

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

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

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

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

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

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

Young educated farmers could access any WIS because they could read and use most technologies

We ascribed secondary themes to recurring words and linked sub-codes to them.Third, we connected the secondary themes to the information design and delivery criteria according to their definitions.The farmer-to-farmer WIS was also interactive because farmers discussed their observations about the weather.A section of farmers also mentioned the Radio Ada WIS as interactive.At the beginning of the farming season, lead farmers, AEAs, and a host discussed pertinent questions about the seasonal forecast and farmers’ observations.Afterwards farmers were allowed to phone in and ask questions or contribute.We also found that farmers required forecast information with relevance for decision-making.The relevance of information for decision making relates to information that provides relevant agrometeorological indicators, e.g., onset date, agronomic advisories, market information, and so forth.The agrometeorological indicators are suitable for deciding when to plough, sow, apply agrochemicals, and harvest.We found that the content of the private weather forecaster and the farmer-to-farmer WIS had relevant agrometeorological indicators such as onset date, length of the season, and rainfall amount.The agripreneurs, AEAs, and Radio Ada WIS provided bundled agricultural information such as agronomic advice.The involvement of farmers in creating information and incorporating their feedback was a factor that also enabled the usability of the information.This factor also involves the use of farmers’ feedback to address actual needs.Farmers mentioned that the AEAs, the private weather forecaster, and the Radio Ada WIS elicited their opinions.

We identified that information providers’ respect for local values enhanced the usability of WIS.This factor implies that the WIS has local content and reflects farmers’ practices, values,grow bucket and beliefs.This factor is relevant for WIS usability in farming in the Ada East District because it is an area noted for the production of food crops, vegetables, and some fruits for the urban market.The growing demand for specific food crops in the urban market impedes changes in the cultivation of certain crops in response to a seasonal forecast.Therefore, farmers expected information providers to understand their values, beliefs, social-economic characteristics, and practices to tailor to their context.For example, they required WIS to guide them in selecting a variety of tomatoes suitable for a forecast rather than indicate a complete change in crop production.Farmers attached relevance and trust to WIS delivered continuously and provided outlooks on changes between the season or during the day.They expected information on outlook on intra-seasonal changes, but this rarely occurred, albeit that the WIS of the public TV, the private weather forecaster, GMet online, E-agricultural, agripreneurs, and farmer-to-farmer were continuously delivered daily.The timing/schedule delivery of WIS is relevant for farming in the district, as some farmers showed interest in seasonal rainfall onset date and 1–14-days forecast to determine decision-making, e.g., when to apply fertilisers.Another aspect of the time factor was the strict delivery of information at specified times.With the attachment of schedules to the provision of information, farmers would have made certain decisions before it was delivered.Farmers noted Agripreneurs’ WIS for providing daily information where the expected forecast was stated with terms such as “expect rainfall in the morning, afternoon, or evening.” Farmers also appreciated the private weather forecasters’ information because of the provision of outlooks whenever necessary.Farmers explained that only a few received AEAs’ WIS directly through a home visit, mobile phone calls, workshops, and field demonstrations.Often, the invitation on AEAs’ WIS to farmers to attend workshops and field demonstrations was limited to one member per household or to a lead farmer on the assumption that they would share the information; yet, sometimes, it rarely happens.

With such selection criteria, women, young farmers, and other groups of farmers were prevented from accessing relevant WIS.The private weather forecaster’s WIS was accessible directly to only a few farmers because the provider could not respond to their calls at all times.In the case of Agripreneurs’ WIS, farmers had to subscribe to a short code to receive the information, and this required training or some level of literacy; thus, it was used by a few farmers.Lack of ‘free time’ because of engagement in various social-economic activities affected women’s access to WIS, especially regarding scheduled information delivery on the radio or TV.Further, the accessibility of WIS for diverse groups of farmers was also dependent on the availability of radio, mobile phones, television, internet, and electricity.The absence of language barriers also enhanced the usability of certain WIS.According to farmers, most WIS were provided in English rather than in the Dangbe language, which is spoken in the Ada East District.Hence, some farmers, especially illiterate ones, were limited to using certain WIS like the farmer-to-farmer WIS.Of the ten types of WIS found in the district, only half – the AEA, farmer-to-farmer, Radio Ada, private weather forecaster, and the public radio WIS – were delivered in the local language.When WIS was presented at length, farmers were no longer able to remember all the information.The provision of WIS on rainfall occurrence was best recalled, whereas other aspects such as the level of uncertainty, location, and other expected conditions were rarely remembered.This challenge was attributed to the presentation of the format and the content of the information.The Radio Ada WIS was sometimes communicated in drama, and it was deemed relevant for farming because farmers were able to comprehend the message.Agripreneurs’ and online WIS were presented in formats such as: “rain likely, tomorrow, rain likely,” “above normal,” or “near normal.” The public TV WIS was presented with maps and symbols indicating sunlight, rainfall, cloudy conditions, thunderstorms, etc.The use of symbols was meaningful to farmers, especially the symbol for rain or sunlight.Some WIS was also packaged mostly as numbers and text.

The terminologies used in WIS presentations required some explanations to aid its usability.For example, although Agripreneurs’ WIS was delivered in English.A structured text message was delivered in the same format to help farmers understand.The use of multiple media, including voice-based, call centre facilities, mobile phones, radio, and text for WIS delivery, was considered to enhance or obstruct the usability of WIS.We found that farmers had a clear preference for information received through voice mode: face-toface interaction, telephone calls, or interactive voice response with this particular factor.Some farmers emphasised the importance of the public radio and the Radio Ada WIS, as the radio could be operated with a battery, had wide coverage, was portable, and was also a mobile phone component.The district did not promote the use of interactive voice response and call centre facilities attached to Agripreneurs’ WIS.The two-way WIS delivery mode allowed farmers to ask questions and receive feedback.The delivery of two-way information was considered vital because it enabled farmers to verify their observations and discuss differences in the forecasts with information providers.The farmer-to-farmer, the private weather forecaster, and AEAs’ WIS provided two-way information delivery through mobile phone and face-to face interactions.Accessible level and mode of payment indicate farmers’ preference for prepaid or free access WIS.In some instances, the fee for WIS deterred some farmers from sourcing certain WIS.Except for public TV, public radio, Radio Ada, AEAs, and farmer-to-farmer WIS, which provided free information, other types of WIS involved some form of payment.

Farmers who were willing to pay for WIS mentioned detailed, reliable, accurate, and evidence-based conditions for farming.In the above sections, we analyzed the types of WIS, the factors that affect their usability, and how each WIS met a specific factor.These analyses are summarised in Table 3, with a tick indicating how farmers perceived a specific WIS to have met each factor.In this study, we identified ten types of WIS for farming in the Ada East District, Ghana.On average, a farmer used at least two types of WIS.The farmer-to-farmer WIS was often used and other types of WIS,dutch bucket for tomatoes indicating a local way of integrating weather forecasts.This finding was also identified by some other studies, which mentioned that, despite the provision of scientific weather/climate information services through the radio, SMS, TV, agrometeorological bulletins, and so forth, farmers complemented forecast with their local environmental observations.The main reason farmers combined different WIS was the need for reliable and accurate forecasts, which seemed absent in a single WIS.Patt and Gwata and Nyadzi also observed that farmers’ use of seasonal climate forecasts increased when combined and compared with local knowledge.The essence of this finding from the study conducted in the Ada East District is an opportunity to co-produce WIS by integrating farmers’ local knowledge with scientific forecasts to enhance their usability for farming.This idea is increasingly discussed theoretically in the climate information service literature.It is necessary to involve existing preferred WIS sources such as farmers, the private weather forecaster, AEAs, and Radio Ada, from the study district.We identified new factors that affected the usability of WIS in our study district.These include the origin of information, continuity of information provision; schedule delivery of WIS; evidence-based information; format and content of information; graphic presentation, symbols, and terminologies, and accessible level and mode of payment.These findings suggest new factors may be attributed to several issues, including climate change and increasing variability in weather conditions, exposure to different WIS and new ICTs, changes in farming practices, and intensive cultivation of crops.

These factors may play multiple roles in triggering farmers to prefer certain factors inherent in WIS information design and delivery.This finding reiterates that the usability of weather/climate information needs to be mobilised around a particular social-cultural context.Hence, the delivery and uptake of forecast information must be context-specific.The findings on emerging factors indicate the need for information providers to make extra efforts to design and deliver WIS to decrease or even eliminate the WIS usability gap for farming.In our study, we observed trade-offs among factors that affected the WIS usability for farming.For instance, we observed trade-offs between predictive skill and spatial resolution.This is because if information providers attempt to attain location-specific forecasts , weather models tend to lose accuracy and vice versa.Despite advances in forecasting, predictions still carry high degrees of uncertainty depending on various factors such as the variable that is being forecasted, the time of year the forecast is issued, the region, and the length of lead-time.Towards this end, Dilling and Lemos indicated that in a context where decision-makers are made aware of the uncertainty inherent in forecast information, they can accept it as part of using the information in their decision-making.In contrast, there are instances where decision-makers may be risk averse and vulnerable.Hence, they may prefer not to use forecasts.In Burkina Faso, individuals were not interested in relying on forecasts until proven reliable.They expected the forecast to corroborate their observations.Other trade-offs identified in our study involve the factors, high level of interaction, and accessibility for all audiences.It was only the farmer to-farmer and the private weather forecaster’s WIS which met this need of farmers.This finding was also identified by Nyamekye et al.in the Northern region of Ghana, where farmers mentioned their preference for the weather/climate information delivered through the radio since it reaches a large group of audiences in the local language.Yet, it does not grant farmers the opportunity to ask questions or even make contributions due to limited time slots allocated to the radio program.We also observed a trade-off between evidence-based WIS and accessibility for all audiences because it was impossible to include every farmer in the district in practical WIS workshops.This finding also follows other studies.These studies also indicated that farmers have preferences for evidence based information delivered through agricultural extension workshops.Yet, the forecast information is unable to reach variable groups of farmers due to gender norms and expectations, patriarchal values, time poverty, the intersection of seniority, religion, class, and positions within households, that intersects with the criteria for the selection of lead farmers under extension delivery program.Trade-offs concerning factors that affect the usability of weather/ climate forecasts have been identified in the literature.They are inevitable in providing weather/ climate information services.Hence, we recommend that information providers engage farmers through workshops or training programmes to explain how trade-offs are associated with WIS.For example, issues on the provision of location-specific and accurate forecasts need to be discussed with farmers to moderate their expectations.

The territory is usually determined based on the status of the family group or family clan

The implication of the cultural context in its development plays a very important role in human life.It acts as a connector of the rule of law determined by the values or legal culture that is internally lived by the community.Likewise, in the entire cycle of farming, there are values of togetherness and the cooperation implied on it.Therefore, farming system is a system in the Dayak society to maintain their life instead of preserving their cultural custom, tradition, and art.The system is also a way of defending their territory by marking the area where they live by replanting various folk crops.The important point of this research is to spotlight the farming management of Dayak people community in maintaining and preserving natural ecosystem equal with the values of local wisdom from generation to generation.This research used a qualitative approach in which the techniques of data collection used direct observation.The observation process was carried out by seeing and observing directly the events occurred in the Dayak community.During the observation, researcher wrote and collected the data in the form of field notes.Also, the researcher recorded whole events related to the farming process occurred in the indigenous society.In addition to the direct observation process, the data collection process was also carried out by collecting secondary data.The secondary data used in this research were government reports which were reported periodically in public.Other secondary data used in this research were also in the form of field documentations such as photographs and field notes written directly by the researcher on location.Furthermore, all data collected were processed by data coding first.Then,nft hydroponic the data coding process was done by taking into account the available data categorization before the data was interpreted.

The interpretation process used Kroeber and Kluckhohn’s approach in relation to the culture cycle.The final stage was the process of data presentation.Kroeber and Kluckhohn stated that there are seven aspects of human culture which consist of language, knowledge system, social organization, living equipment and technology systems, livelihood and economic systems, religion, and art.Regarding the farming of Dayak people, it can be seen through the whole process, sequence, harvesting yield , and the peak of farming cultivation as the cultural system.Rice is the primary food of the Dayak people, which is the main source of life for generations.Farming is not merely a system of livelihood and economy, but also the form of knowledge system, social organization system, living equipment system, livelihood and economic system, religion, and the occurrence of art substance in it.Related to the culture, we also recognize the existence of stages in the development of the livelihood and economic systems from time to time.According to Alfin Toffler , there are three waves of human livelihood and economy from time to time, those are Nomad, Agriculture, and Industry/Information.To protect various important assets inherited from ancestors who have been accustomed to passing on the social order system and the assets of indigenous peoples from generation to generation, the process is always based on a system influenced by the cultural domain.The interrelation of cultural domains plays an important role in the process, the system and concept that develop in the social order of rural communities or indigenous society groups.We have passed the first stage when humans are no longer moving from one place to another, or nomads.In this first wave, the needs of human life and their social changes are not yet so complex.In such a way, it can be said that the livelihoods and economy of humankind in the nomadic era are still very simple.Then, entering the second wave where livelihoods and economy rely on agriculture humans have begun to settle in a certain area.It is believed that the agricultural system by burning the land has been started since this first wave, around 10,000 years BC.

As stated by Lubis , “Until today in our country there are still two-million people in Sumatra, Kalimantan, Sulawesi and other islands who have made their living with farming technology since around 10, 000 years before Christ”.Meanwhile, the third wave is the stage where humans enter a new civilization named a livelihood and economy based on industry or information technology which is marked by the emergence of factories, companies, information technology, and even now industry 4.0.If we take a look at these waves and stages, there is a phenomenon which is more or less the same where in every wave of the human livelihood and economic system there is a static system , but some is dynamic.The dynamic one is generally related to technology, speed, form and structure of society, social class and societal strata that we know as the social change.The practice of farming only occurs in certain communities whose large territory and are still not much reached by industries, such as in Kalimantan, Sumatra, Sulawesi, Maluku, and Papua.On the other hand, there is a growing awareness that the value of indigenous community’ forests is much higher than the temporary economic value, for example for mining, plantations, or for building housing and offices.”For the customary community,forests and sea as well as other natural resources in their customary territories have high economic values.Not only that, natural resources in their customary territories are the center of social cycle, cultural and spiritual activities.Essentially, this is related to the effort to preserve nature which does not only provide concrete consumption products such as food, but also ecosystem services which become the enabling factor for the sustainable production process”.Observing the sustainability of the environmental ecosystem in the forest areas of the customary society in Kalimantan, we may view from the perspectives of the natural resources where people live and exist for generations.In Masiun’s study, he calculated the economic value of customary forests owned by the indigenous community of Seberuang Riam Batu located in Tempunak District, Sintang Regency, West Kalimantan Province.Besides practicing subsistence economy, the people in Riam Batu have also followed an open economy system.

However, the people do not want to sell their customary forest for various momentary benefits because they realize that the value of forest is much higher than mining, plantation, housing, and others.The Dayak people also implement the loop back farming system that returns the plants back to their original cycle based on the natural law within 15 years.That all laws are created through some kind of social process; a conventional norm is the outcome of something resembling a deliberative convergence of behavior and attitude on the norm, while other social norms are manufactured through social processes like those set forth by a rule of recognition and imposed on non-members of the group.This only likely happens since the customary community manages their forests wisely and place their entire process and livelihood system as a sustainable system.Thus, the farming systems of the Dayak people are well-integrated with nature and its environment.The way of being and the way of life of Dayak people cannot be separated from the nature and the environment where they live, reside and exist.In the past, from various literatures and research conducted by foreign authors, many things have not been revealed to the surface related to the wisdom, insight, and values in the farming system of the Dayak people.Morrison , David Jenkins and Guy Sacerdoti , for instance, tend to view in general the cultivation of the Dayak people in Borneo merely to produce rice.Morrison acknowledges the importance of farming for the Dayaks while pointing out that rice is the staff of life for the people.Rice is so important to the Dayaks in Borneo, so that Morrison writes the title “Padi – The Staff of Life”.It describes how the Dayak people obtain rice, starting from clearing the land to getting feast together after harvesting.Meanwhile, David Jenkins and Guy Sacerdoti calculated that each family head of Dayak people who cultivates one hectare of land will yield roughly 900 kg of rice.This is, according to the Western’s perspective, considered unequal between the woods cut down and burned becoming charcoal, and the results gained from it.However, if we observe carefully that the farming of the Dayak people is not solely and only rice as a target to be yielded.Farming for the Dayaks is not just a rice cultivation.A lot of wisdom, values, customs, traditions, culture, arts, even economic and educational values are enclosed behind it.Researchers and authors from “inside”, known as the intellectuals of the Dayak people, have tried to describe the hidden dimensions and tacit knowledge that outside researchers have never seen, written,nft system and even published them.In such a way, what ‘insiders’ have studied and written seems to be considered correctly because there are no other research results and publications arguing or adding other elements of farming rather than rice as its novelty.

Yansen notes that the environment, forest, and farming cannot be separated from the activities and the life of customary or traditional communities.“For hundreds of years, the ancestors of the Dayak people have a forest area as their territory.They continue to develop and to build evolutionarily cultural and social characters in line with their interactions with their nature and environment.The environment and nature shape various social models and customary territorial boundaries of the Dayak people, such as hunting and farming activities.These two activities can determine and legitimize the right of their customary territorial.This cultural and customary model has been institutionalized, accepted, maintained, and conserved from generation to generation by individuals, customary communities, or customary institutions even by village bodies.Thus, it is implicitly explained that there is a social function of the forest.On the other hand, throughout the farming process there is a dimension or activity that includes or involves many people during the process.According to Kroeber and Kluckhohn the cycles or stages of farming of the Dayak people integrate the management of ecosystem and the traditional culture of Dayak community.In general, the stages of the farming found in this study are: inspecting the land, determining the land area, cleaning or purifying farming tools, slashing, cutting the trees, burning the land, planting, weeding, harvesting, and performing thanksgiving ceremony.Those ten stages of farming are applicable everywhere among the Dayaks and those are mandatory to get through.However, there are some practices or other activities in some places added by the clans or customary communities in the process.It is quite interesting to observe as a social exchange process where the stage becoming the crown or the peak of the farming system and cycle is the thanksgiving ceremony or Begawai.It is not only in a village that people festive the ceremony, but also it involves the nearby villages, or even likely villagers from other areas who have an interest or still have family relationship with the host of the event.The farming or cultivation is carried out once in a year and simultaneously in the season which is considered to be the right time to start the opening of farming activities.When farming is done in a group and together, pests and crop diseases will be avoidable.Or if pests and diseases attack crops in fields other than rice, their attacks are still within tolerance limits since there are many fields to be affected.Therefore, pests and diseases can spread over to the large areas so that they do not affect just one field which can cause mass destruction.In certain Dayak tribes, for example the Dayak Lundayeh in Krayan of North Kalimantan, there is a well-known tool to determine the right season to start the cultivation named “Batu Tabau”.It is a kind of traditional tool to see the direction of the sun rotation.Meanwhile, among the Dayaks in Kapuas Hulu of West Kalimantan they start cultivating on their fields by observing the astrological sign.They know the “three-star sign” which give them a sign to slash, to burn, to plant and so on.Among the Dayak people of West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan and North Kalimantan there are similarities in determining to begin the farming cycle.That is, the starting point of the period is to inspect the land starting in May and ending by harvesting in March or April by the next coming year.

Usage of tin and brick materials in wall constructions increased after shrimp farming

The government and non-government organizations must come forward to raise public awareness and the provision of safe drinking water to the coastal communities.An alternative measure of living standards and profitability of the shrimp farming practices was comparing the farming communities household construction materials in coastal areas.The results divulged that before shrimp farming, about 82 % of the households’ wall construction material was mud that dropped to 58 % after shrimp farming.The floor construction was predominately made by mud before shrimp farming that dropped to 78 % after shrimp farming, while the use of bricks in floor construction increased from 8% to 22 %.The use of tin for roof construction increased from 26 % to 66 % before and after shrimp farming, respectively.Instead of capture fisheries, shrimp farming brought significant improvements in the housing construction quality.Over 80 % of hut-like households were reported by Islam et al., which indicated a declining tendency after shrimp farming.The study made it feasible to conclude that shrimp farming has resulted in a substantial uplift of the residents living and housing pattern.Based on previous reports, higher salinity levels in the study area changed the soil quality that turned it unfit to build the house with a simultaneous decline in rice cultivation, causing an immense lack of straw for roof construction.Formerly a rice agriculture hub, this study’s coastal areas displayed a substantial shift from rice culture to shrimp farming.We intended to reveal the hidden reasons for this blue revolution, and the data showed that over a half of the farmers citing the prevailing salinity as the leading reason for this shift from agriculture to shrimp farming.Apart from this, we also looked for other reasons compounding the impact of increasing salinity,flood and drain table and the results showed that salinity and poor rice production , salinity and more income while only 10 % of farmers established the reason for poor rice production.

Akber et al.have reported similar findings in previous studies targeting the same locality.The substantial economic benefit is the primary reason for the increased commercial saline-water Bagda shrimp farming.The saltwater ascension worked as a double-edged sword.It resulted in a decline in rice production while acting as a more profitable farming source for the coastal communities.The saline water intrusion was the prime cause that forced the study area people to shrimp farming instead of rice cultivation.With declining land for grazing and fodder cultivation, shrimp farming has brought overwhelming changes in the patterns of livestock and poultry rearing as well as in the tree production in the coastal areas in Bangladesh.After shrimp farming, the number of people having no cows and goats increased from 14 % to 68 % and 10%– 40%, respectively.It indicated a tremendous decline in cows and goats rearing practices in the study area.On the other hand, where small or livestock raising for personal usage declined, the commercial level farming of cows and goats increased before shrimp farming times.This massive revolution in livestock rearing practices alluded to the potential economic solvency.The number of trees is also considered as wealth that can be utilized in times of emergency.The presence and rearing of trees and poultry birds displayed substantial decline after shrimp farming, and the reason is apparent.The trees provide home and roosting sites to predatory birds, while poultry farming could not have been profitable due to changing climatic conditions and saltwater intrusion.Further, increasing salinity levels could have compromised the suitability of soils to grow trees and seedlings.Previous studies have reported that shrimp farming decreases tree production , especially for more profitable management, i.e., expanding shrimp farms.

Sustainable income brings satisfaction among the farming communities.The percentage of farmers with lower income was higher, having income ranges lower than USD 51–100.It was noticed that the rate of shrimp farmers having an income range of USD 101–150 jumped from 16 % to 36 % after shrimp farming practices.The farmers having more than 150 USD income were only 2%, which soared to 26 % after shrimp farming.It alluded to the sustainable increase in the income levels of the coastal shrimp farming communities.With our findings, we are correct to say that shrimp farming has become a new lucrative business for the southwest coastal inhabitants rather than rice cultivation.The rice and shrimp culture’s annual comparative cost and income are shown in supplementary material Table 4.We also collected the cultivable land prices in the rice and shrimp culture, and findings are presented in supplementary material also.Shrimp farming has brought a significant change in the stakeholders income level.Approximately 72 % have shown absolute satisfaction after shrimp farming, while 4% expressed as very satisfied.However, a 16 % remained neutral with neither satisfied nor dissatisfied, while only 8% showed dissatisfaction after shrimp farming.Previously, all the respondents have expressed their satisfaction status regarding shrimp farming comparing with rice cultivation as previous research.The farmers expressed their opinion based on their present social-economic status and life patterns that may lead to environmental consequences.Those show exhibited satisfaction indicated said that the infrastructure quality of locality is more developed than before.They were able to maintain a family at a medium level and send their children to school.They expressed shrimp farming aided in an increased purchasing power.Some others opined though shrimp farming has benefitted them economically, it leads them to buy all of the commodities they had to cultivate before.Therefore, we can conclude that shrimp farming has become beneficial to the study area as many respondents are satisfied.

In the wake of shrimp farming, an enormous increase has come in the respondents income level compared to rice cultivation.In some cases, it has shown manifolds increase.Nevertheless, the respondents also mentioned that when their gherinfected by viral diseases, it critically affected their earning in huge investments.So, this can be concluded as that shrimp farming’s income could be unpredictable, which is similar to previous studies.This also provides a reasonable explanation for the dissatisfaction among some of the shrimp farmers.We studied the change in income status of the shrimp farming communities after shrimp farming, and the results showed that shrimp farming brought conspicuous changes in the income status.The primary occupations included agriculture shop keeping, labor , fishing, salaried individuals, and private business.The total percentages showed that income levels disclosed a marked increase in the range of 101–150 USD.The income ranges of 51–100 USD and >150 USD obtained a 26 % increase, which can be described as a marvelous improvement in the shrimp farming communities economic status in the coastal areas of Bangladesh.These findings indicated that shrimp farming increased the people’s income in a reasonable way that could be projected to the elevated social-economic status of the coastal communities.We studied the positive and negative impacts of shrimp farming on a scale of 1− 10.The results displayed that the most positive impact was the high profitable business compared to the rice cultivation.In contrast, the highest negative impact was the lack of fodder for livestock.The respondents firmly supported that shrimp farming is more profitable than rice cultivation.Many others believed that due to increasing shrimp farming, there was higher daily demand for fish, increased land value, and increased daily income.However, some mentioned that daily income from the gher is somewhat dependent on other factors as well.The last one among the positive impacts is that shrimp farming required less labour than rice cultivation.Many believed that shrimp farming takes more time than rice cultivation; there is no strenuous effort.All types of impacts are countable and help identify the fundamental problems of shrimp farming.After the lack of fodder availability, 7.44 out of 10 were mindful of destroying vegetation and its effect on bathing or drinking water.Some respondents poorly ranked the lack of employment opportunities due to shrimp farming.

Rearing livestock and cultivation of the homestead garden is an integrated part for the rural households.Nevertheless, saline water intrusion has supplanted the grazing land, which hampered the cattle rearing.We also investigated the overall impacts of shrimp farming perceived by the shrimp farming communities in Bangladesh’s coastal communities.The survey was based on four preordained factors used to assess the respondents overall perception of shrimp farming.The elements used for comparing were rice cultivation, fish culture, salinity, and shrimp fry collection.The participants were asked to express their opinion in five categories: strongly agreed, agree, neither agree nor disagree, disagree, strongly disagree, and these were weighted by 5, 4, 3, 2, 1, respectively.The 78 % of participants strongly agreed that shrimp farming is more profitable than rice culture, while 60 % agreed on its higher profitability than freshwater fish culture.However, 46.9 % agreed that it was easy to enter the saline waters for shrimp farming, while 44 % agreed that it was easy to collect the shrimp fry.Using the weighted index method, the total scores were 237, 214, 188, and 162, respectively, rolling bench for the stated four factors.The highest total score was 237 for more profitable than rice culture, followed by 214 for more profitable than freshwater aquaculture.These findings indicated shrimp farming as a more profitable practice than rice cultivation with other supporting factors.Aquaculture, a vital economic activity, contributes significantly to global nutrition and food security, whose production peaked at 82.1 million tons and sale value was estimated at USD 250 billion in 2018.China is the country with the largest aquaculture producer in the world, accounting for around 58 % of total global aquaculture production, far exceeding the total output of the second- and third-ranked countries combined, of which Pacific white shrimp occupies an economically important position in aquaculture.However, several emerging pathogens, including covert mortality nodavirus , Vibrio causing acute hepatopancreatic necrosis disease , and shrimp hemocyte iridescent virus , etc.have posed many great challenges on the global shrimp farming industry.In the second half of 2020, unusual mortality events of cultured P.vannamei occurred in local farms in Dongying City and Weifang City, China, some diseased shrimp showed symptoms of hepatopancreatic atrophy, midgut empty and shell softening.In this report, we analyzed and detected the pathogens that could be infected by the diseased shrimp and its feed organisms, verified through histology and molecular biology methods, and finally determined the cause of outbreak death of farming shrimp.

At the end of 2020, continual mortality of cultured P.vannamei generally occurred in local farms in Dongying and Weifang City, China.Over 80 % of local shrimp farms have been impacted.In Dec 2020, the author’s laboratory was asked to perform a local investigation into some shrimp farms breeding white leg shrimp.Four indoor semi-intensive aquaculture farms were visited.It is understood that greenhouse aquaculture is one of the important local aquaculture modes, and underground brine is an important source of water for aquaculture due to the northern part of the city is located in the coastal area.The aquaculture water was aerated with air stone, the water temperature was 28–30 ◦C, and the salinity was 18–25 ‰.During the breeding period, the shrimps were fed with mixed bait and frozen bait.The morbidity of shrimp was characterized by continual death.The onset time mainly occurred in the two stages of shrimp larvae population separating and shrimp juvenile population separating.The final density was 500–1000 individuals/m2 after the shrimp larvae population separated.Mortality would be observed to start 3–7 days post-transfer.At the beginning of the disease , the number of shrimp deaths was small, but the number of shrimp death reached 100–150 individuals/pond after 7 days.Shrimp death continued, with the high number of dead shrimps exceeded 150 kg/pond 3 days after the onset of illness in some adult shrimp farms.The diseased individual of the P.vannamei showed obvious clinical symptoms, including hepatopancreatic atrophy with color fading, empty stomach and guts, shell softening.Mild muscle whitening and necrosis occurred in most P.vannamei individuals in the VCMD case, and a few diseased individuals that being at the acute stage showed obvious large proportion whiteness of abdominal segment muscle.Meanwhile, the diseased shrimp was weak in vitality and usually sunk to the bottom of the pond without moving.What’s more, shrimp grew slowly on some farms.All samples were amplified and prepared for sequencing using a two step, reverse transcription nested polymerase chain reaction protocol with two pairs of primers.The procedures and primers used were identical to those described as reported previously.Following amplifications, products were separated in an agarose gel electrophoresis and bands were sequence verified at Sangon Biotech Co., Ltd.The sequence was identified through BLAST searches, and the deduced amino acid sequences of CMNV target RdRp gene fragments from positive samples and RdRp amino acid sequences from other nodavirus were selected for phylogenetic analysis by using MEGA X software.

The specific transformation pathways that farms take can be conceptualised in terms of resilience

Resilience refers to the capacity of social-ecological systems to fulfil their function in changing conditions, thus withstanding disturbances and being able to adapt and transform while delivering on their main goal . Although resilience is sometimes portrayed as stability, resilient systems can—and should be able to—transform. The strategies through which a social-ecological system may retain its resilience can be characterised in terms of persistence or robustness, adaptability, and transformability . Robustness refers to the capacity of the system “to withstand stresses and anticipated shocks” . Adaptability, in turn, entails “the capacity of actors in a system to influence resilience” by, for example, changing “the composition of inputs, production, marketing and risk management in response to shocks and stresses but without changing the structures and feedback mechanisms of the farming system” . Lastly, transformability is about “the capacity to create a fundamentally new system when ecological, economic, or social structures make the existing system untenable” . Such changes can imply a changing function of the farming system . A farm system may employ different resilience strategies over time. The food system and the embedded farm systems are in a flux of constant interaction: the dynamics on both levels condition each other. The employed resilience strategy depends on the transformative capacities of the farm and the farmer—what they can do with the resources they have. This makes resilience a question of agency and power. In a situation where the regime is strongly locked-in, farmers’ choice space becomes substantially limited .

The pressures are manifest in how farmers are acting mostly as price-takers and carry the responsibility for mitigating environmental impacts in the food system . However,flower pot not all farmers are similarly affected by transition processes, which calls for analyses of the transformation pathways accessible to farms. Agency and power are longstanding areas of research in social sciences. Agency can be seen as the actors’ capacity to act, and it constitutes power, intentionality, freedom of choice and reflexivity . Power, in turn, is understood here as “the capacity of actors to mobilise resources and institutions to achieve a goal” . When resilience is understood as the capacity of a system to achieve its goal, the notion of power in achieving that goal is central to the analysis of resilience. Resilience requires adaptive capacity, which refers to the potential of system agents to fulfil their goals, act independently, and exert their own agency . As such, the concept of adaptive capacity is practically identical to the concept of social power. Analyses of resilience and adaptive capacity at the level of farm systems require identifying the kinds of goals farmers hold regarding food production, the resources available, as well as the capacities to utilise them to achieve those goals . Thus, even though the concept of resilience has sometimes been used without being attentive to the societal context, questions of regime reproduction, or social power , it holds potential in analysing questions of agency, power, and social justice related to systemic transformations As systems may employ very different strategies to retain their resilience, it is presumed that system actors also employ different capacities in accordance with their resilience strategy. Avelino argues that transformative capacities are different from capacities that reproduce the existing structures, as in the case of persistent or adaptive versus transformative types of resilience.

According to Patterson et al. , “Transformative adaptation approaches take as a starting point that power relations condition the options available to marginal and vulnerable groups to shape their own desirable futures, thus requiring keen attention to issues of social difference, power, and knowledge.” Tribaldos and Kortetm¨ aki see capacity development as a criterion for a just transition in the sense of whether food system actors can respond to transition pressures. Thus, resilience capacities depend on what people can do and be with those resources and goods they possess or have access to . How farmers as system actors employ their capacities is a function of their internal goals and the external conditions defined by the food system . When the distributive effects of external conditions fall unequally upon the food system actors, restorative justice can reveal new perspectives on mitigating these effects. Restorative justice approach is traditionally understood as a non-adversarial response to harm and conflict that derives from violations of law, rules, ethics, or a general sense of moral obligation . The concept originates from criminal justice studies seeking to repair the damage and restore the dignity and well-being of all those involved in causing harm . However, restorative justice has increasingly been acknowledged in the field of sustainability, particularly from the perspective of energy transition, nature conservation, food transition and human rights . The common characterisations of restorative justice emphasise face-to-face dialogue between different parties configured as offenders or perpetrators of harm and the subjects-of-harm . The latter is often conceptualised as a “victim”, a condition under which agency and relationship with offenders are to be transformed. The process of restorative justice involves a reactive mechanism to address the damage already done. In other words, the process seeks to restore justice within the structures of the existing system. Accordingly, the individual is expected to undergo a transformation process while the surrounding system does not change.

Recent proactive approaches to restorative justice have emphasised more anticipatory elements of restorative justice. This means involving a range of actors and adopting a forward-looking approach that is both preventive and strategic . However, to be genuinely proactive and transformative, justice cannot be achieved by restoring the status quo ex ante . We further argue that the main challenge of restorative justice during systemic changes is that the transformation is not only about individuals but the system itself. Thus, individuals cannot be easily ‘restored’ with the logic of a system on the move. In systemic transitions, this would mean that those at risk of becoming ‘transition victims’ should also have the opportunity not to become ones. However, the application of the restorative approach to sustainability transition is not unproblematic, as the actors who fall victim to the transition processes have at the same time contributed to the problems that call for a transition in the first place. To what extent this contribution can be credited to the deliberate choices of the actors or just to them operating by the rules of the game remains debated. However, the current financial position of farmers suggests that the system itself is the most crucial factor in delimiting their choice space. The just food transition poses a fundamental challenge to restorative justice; the food system itself is enduring a major transformation which is also expected from the actors within the system. We argue that a genuinely transformative and proactive approach to restorative justice should aim at resilience and capacity building not only in terms of the existing system, but also in terms of the systemic transformation. We now move on to examine farmers’ transformative capacities and then discuss our findings from the perspective of restorative justice. The research area in Eastern Finland comprises three provinces: North and South Savo and North Karelia . The area is characterised by a sparse settlement structure and rather unfavourable socio-economic development patterns. The area adds up to 18% of the total area in Finland and 10% of the total population, with 557,000 inhabitants.

On average, the farms in Eastern Finland are smaller than the national average, and the fields tend to be fragmented into small plots. The share of utilised agricultural area in Eastern Finland is 5% of the total area in comparison with the Finnish average of 7.4% . The climatic conditions and soil properties are particularly suitable for grass production, and consequently, the role of cattle production is pronounced with 33% of all farms in Eastern Finland being cattle farms in comparison with the Finnish average of 20% . A significant share of the yields produced on crop farms are used for feed on cattle farms in the area . Regarding farm sales,berry pots in Eastern Finland 68% comprises animal products in comparison with the 58% average of mainland Finland . This study is based on survey data collected during the mid-term evaluation of the 2014–2020 Rural Development Program of Eastern Finland . The programme addresses a wide range of social, economic, and environmental issues of farms and rural areas by channelling the funds of the second pillar of the EU’s Common Agricultural Policy for farmers, rural firms, and non-profit organisations. A survey request was sent to all farmers in Eastern Finland who had received agricultural support from the programme and who had registered an email address in the IACS farm register . All active farmers in Eastern Finland with at least 5 hectares of arable land are entitled to LFA support, and in Finland, the support encompasses nearly all agricultural land . As a result, 577 responses were retrieved, with a response rate of 9% despite several requests to fill out the questionnaire. The low response rate was partly due to unfavourable timing of the survey at the beginning of spring but is in line with many recent farmer surveys conducted in Finland. The survey addressed issues related to the farm and its production activities, the farmer and the farming family, farming as a livelihood, environmental aspects related to farm management, and the main types of subsidies received and their perceived effectiveness. The basic characteristics of the surveyed farms are presented in Appendix 1 in comparison with all farms in Eastern Finland and all farms in mainland Finland. The survey respondents farmed slightly larger farms than farmers in the area on average but were broadly representative of farmers in the area.

Most of the survey respondents were cattle farmers , followed by other crops and cereal production . Garden crops, especially strawberry and currant, are typical crops in eastern Finland and had a share of 9% in the dataset. We operationalised the concept of resilience according to the three dimensions of resilience: persistence, adaptability, and transformability. In addition, we also identified a non-resilient group. The operationalisation strategy was based on three variables: 1) the future strategic orientation stated by the farmer , 2) an additional open question related to the farmer’s strategic orientation asking the respondent to specify his or her plans, and 3) freely expressed goals for farming . Out of the 577 responses, 575 were analysable in terms of resilience; thus, the final dataset consisted of 575 responses. Coding farm resilience was an iterative process between the three variables. Table 1 presents the coding principles for each resilience group. In short, a farm was coded as persistent when the farmer aimed at business-as-usual and did not indicate development intentions. Those farms that aimed at developing the farm within the existing operations were coded as adaptable. Transformable farms indicated a deliberate search for a new direction for the farm business by diversifying the farm operations or doing something new in comparison with the existing operations. Non-resilient farms aimed to quit farming by retirement or moving into another business; they did not have successors and their intention was to lease or afforest the fields. The resulting four farm groups with diverging resilience orientations were profiled in terms of variables concerning the farm and its production activities , the farmer and the farming family , farming as a livelihood , environmental aspects related to farm management , and the main types of subsidies received and their perceived effectiveness , adoption of agri-environmental contracts, investment support, organic farming, extension support. These variables reflect the availability of resources, as well as how farmers make use of them and how they relate to environmental management at the farm level, reflecting the mobilisation of environmental values and motivations. A complete list of the variables included in the analysis is given in Appendix 2. To determine whether the differences between the resilience groups were statistically significant, ANOVA tests were performed for continuous variables for the comparison of means, and contingency tests were performed for categorical and dummy variables for comparison of the distributions.

Reproduction control is another important tool for flock management in dairy sheep

With average costs of roughly 1.50€ per each tag, it is the cheapest method among the three. However, it suffers from one disadvantage which could lead to several problems. Its application to the ear lobe of the sheep increases its possibility to be lost due to entanglement in bushes, trees fences, etc. Another problem has to do with the ease of removal of the tag, a practice used in various fraudulent activities regarding animal identifications and could be avoided using irremovable animal tagging systems. In case of tag losses, new tags are to be applied, which not only causes additional administrative work but also impacts the welfare of the sheep which have to undergo another piercing of the ear.In this case, the EID is enclosed in a ceramic bolus, which is then inserted into the sheep’s rumen using a designated tool . Although having a slightly higher cost of about 4–5€, its main advantage is its permanence and very low malfunction and loss rate. Boluses have widespread use and are currently applied routinely in many commercial farms. It is however a more complicated EID to insert, with sheep needing to reach a certain age in order to safely receive the bolus. Size reduction and proper insertion by trained personnel mitigate these problems, with the bolus total size and length being a key factor. As shown by Hentz et al. , smaller boluses could be inserted safely and efficiently to smaller ewes while retaining the internal positioning and reliability.Widely used in house pets and horses for animal identification, its use in livestock although permitted is very limited . The main reason for its limited use is the difficulty to remove the EID in the abattoir,and the tendency of early models to migrate from the original region of injection.Different studies however show limited migration patterns of modern glass and silicone enclosed injectable EIDs during their use in field conditions.A particular advantage of injectable EID is the possibility of it being used not only as passive information storage but also as a sensor for physiological parameters.

The use of temperature detecting injectable passive RFID/ EIDs is widespread in the management of smaller laboratory animals and was tested on bigger farm animals under different conditions . Its use in sheep has been shown to provide highly correlating data to that of core temperature measured via rectal thermometry. This concept is, however, stackable flower pots still in the experimental stage and its future applications are uncertain.Sensors applied on the individual animal are one of the key principles of PLF with tools such as pedometers and rumination tags are well known to dairy cattle farmers. They provide information on animal’s physiological conditions whether in real time or via data loggers downloading in key passages . These sensors collect data from the animal and translate it into physiologic status such as ovulation or lameness relevant to farm management . In case of extensive sheep farming, wearable sensors have been experimented in small-scale-controlled conditions as well as experimental farms . The main objectives of these sensors are to evaluate grazing and resting behaviours, which provide information regarding grazing patterns and feed intake as well as animal position and movement of the flock . Currently, two main types of technology are being tried in this field: accelerometers, especially the tri-axial type, and GPS systems. The third use of active sensors is in the case of social networks and behaviour such as heat and mating identification. Being a seasonal breeding species, a big focus in Mediterranean production is dedicated to out of the season mating in order to maintain constant milk production in contrast to the sheep’s natural cycle . Currently, a common practice is the use of a harness on the flock rams with colour for visual identification of covered ewes; however, the use of electronic activity logger is being tested .A system that measures movement in terms of the direction and speed of the sensor is attached to the foot, neck or head of the sheep. Evaluated by the software first, data are provided to the producer to assist in decision making .

The most useful data come from three axial accelerometers which record movement in a three-dimensional pattern. Field trials confirm the ability of such accelerometers to register movement patterns linked to behaviours such as resting, grazing, moving and running/playing or lameness . Even though accelerometers could be considered technologically matured, data interpretation and validation is still a subject for field research . Meanwhile, the collection and management of the data as well as energy supply to systems in the field present a big challenge for a widespread application. In recent years, the amount of research put into this system is growing increasingly especially in attempt to take a research ready prototype into commercial production . Therefore, accelerometers could represent in the near future a viable product.Especially when paired with geographic information system , it provides information on animal movement and disposition in certain geographical areas. Such a system could help evaluate the movement of sheep in a vast grazing area, between water sources, low and high land and in response to the presence of predators or wild herbivores . In the work of de Virgilio et al. , combined use of accelerometers and GPS/GIS was proposed as a PLF option for sustainable range land management. Such systems, however, are not yet operational in commercial farming due to relative high cost of each sensor and the need for high energy supply . Also, information gathered by the systems still needs interpretation and given the right value in a decision-making process.In a recent study by Mozo et al. , tri-axial was used accelerometer with specific software to detect rams’ mating activity providing a possible tool to measure service capacity of rams. A more mature system is the electronic Alpha-Detector which includes a harness for the ram with an active reader and transmitter which detects the ewes’ EID and transmits the data to a centralized computer. The transmitted data could be interpreted for frequency of mating, true and false coverings and the number of ewes covered. This system has currently passed the research phase and is being tried in field conditions for commercial production .

Other technologies include a concept produced by Laca regarding extensive management of animals which incorporates GPS, satellite communication of data from ‘mother collars’, short distance communication between the animals’ collars and feed management based on the elaborated data. The system is very complex and requires both costly technologies and knowledge of the herd dynamics for the identification of key individuals in their respective groups . The feasibility of such system is becoming widespread in Mediterranean dairy sheep farming due to cost and complexity, but may be relevant for other types of extensive farming that use larger grazing areas , or less contact with the animal . Other sensors include microphone and sound analysis of chewing sheep and monitoring urination in sheep and cattle in order to determine liquid and nitrogen emissions. However, the systems were only described as an experimental process and not yet ready for field implementation.Stationary sensors are another key element in the PLF concept, with different types of sensors such as temperature sensors, cameras, weights and automatic feeders are placed in key locations of a barn . These sensors collect data and usually communicate with the animals’ EIDs, providing real-time data for each single animal to feedback systems . In extensive sheep farming, there are several stationary tools such as AD, weighting crates or a walk over weight system. Although the systems are extensively tested and reached advanced stages of development, they are not yet accepted by dairy sheep farmers for widespread commercial use .An AD, in simple terms, is an automat system centred around a selective gate with the ability to distinguish and direct the passage of animals. Most of the AD systems are based on the recognition of animals’ EIDs as the selective criteria. In extensive sheep farm, ADs and EIDs could be used together not only for data collection and feeding control but also as a tool to reduce manual labour for the flock . Animal selection is one of the most labour intensive activities on the farm, especially in events such as sheering, parasite treatments and selection for sale.

Automatic drafters could also be coupled with weighting systems in order to measure the condition of a single sheep, directing lower weight animals towards supplemented feeding areas accordingly .Originally developed for grazing cattle, both systems were consequently adopted and modified of sheep farming as well. The WOW was tried in field conditions where it proved its efficiency, consequently expanding its use to sheep management . The system includes a one-way passage leading to a key stimulant which the animals are forced to pass through. The weighting platform is placed in this corridor, and it communicates with the animals’ EIDs on each passage. Data regarding each single sheep are stored and could be matched against similar passages in a single day creating a more reliable result. When used by itself, the WOW system helps to reduce labour with fewer personal needed for animal sorting activity while pairing it with AD systems can allow better control on supplementation feeding . This combination has been proven to be efficient in several studies as presented by a recent review by Rutter and by Gonzalez-Garcia et al. , making it a viable instrument for farm management. The WC on the other hand is used by actively separating single animal by operator closing doors in a passage corridor. This way, each animal is weighted standing still and isolated from others. In the WC, the RFID identification could be done both by handheld transponders or by fixed reading antennas,flower pots for sale consequentially allowing the analysation of data in real time. Commercial models are already available on the market . The collected data could be used for various purposes such as ensuring lambs are ready for sale or anthelmintic treatments. The last use is of particular importance considering the growing awareness to the amount of anthelmintic resistant parasites in grazing sheep and the health implications derived from it . For this purpose, coupling the WC with a self-dosing fluid dispenser is a currently viable option with commercial products already on the market such as Te Pari fluid dispenser .

Virtual fencing is an innovative method for extensive animal management that replaces physical barriers with electronically placed boundaries. Animals are prevented from passage by a system of visible and/or audible cues combined with electric stimulus. Although VF is not able to provide a full sealing of an area, its flexibility and potential applications has attracted a growing amount of researches as well as stimulating commercial development with products such as BoviGuard, NoFence and eShepherd™. The main advantage of such a system is not the complete exclusion of animals from certain areas, but rather the possibility VF provides to guide and move the animals according to pasture availability . However, VF cannot completely replace all fences, as the hermetic exclusion of animal is impossible without physical barrier. Therefore, due to security reasons and property rights , the external fences of the pasture remain necessary. By using visible and audio cues prior to the electric stimulus VF systems are aiming to condition the animals to understand the limits of their area. Although there is a variability among the individual animals in understanding these limits, as a group the herd maintains its position . There are several factors however, which limit the adoption of VF systems on commercial farms. The first is its cost, although the cost of the system was estimated in 200 000 £ for 100 animals in UK, its difference is not as big in comparison with traditional fencing costs in the same country . However, VF cannot completely replace traditional fencing and a combined use will be always needed . Another weakness is the lack of technological infrastructure in sheep farms ; this includes network coverage and IT-related skills and understanding. Without this, farmers may find it difficult to trust hi-tech systems .