Work safety interventions and threat complexity - A formative investigation into why farmers do not act safely

INTRODUCTION

Fear appeals are a common tactic used in work safety interventions to motivate farmers to adopt safer behaviours. They begin by introducing a threat, followed by information on how to remove the threat. However, fear appeals tend to be ineffective when developed without a firm grasp of the cognitive processes underlying behavioural change. Although previous research on farm safety interventions have investigated fear appeals, they have focused on very narrow threats and behaviours, such as tractor or cow safety, while others have studied the threats but not the cognitive processing. Consequently, not enough is known about the range of threats that evoke fear, how farmers behave when under threat, or their general cognitive beliefs regarding self-efficacy, response cost and response efficacy. In In this study, 23 Swedish Farmers were interviewed and participated in a work safety intervention to identify the range of threats farmers perceive, and actions taken to remove those threats.

MATERIAL AND METHODS

The extended parallel processing model was used to gain insights into how farmers cognitively processed threats and their subsequent behaviour. Interestingly, it was found that farmers were more fearful of work safety threats related to family members and employees-yet the actions they took to reduce threats were mostly personal in nature. To help explain this finding, a typology of threat complexity was developed by the authors.

RESULTS

It was found that simple, common, and direct threats to safety tended to lead to adaptive, threat-reducing behaviours, whereas complex, general, or indirect threats promoted more maladaptive behaviours that reduced fear, but not the threats.

Survey in Nurseries and Irrigation Water Reservoirs as Sources of Oomycetes Found in Avocado Orchards in the Canary Islands

Nursery stocks and irrigation water are important sources of Phytophthora spp. In this study, 20 irrigation water reservoirs and 10 avocado nurseries were surveyed in the Canary Islands between 2013 and 2015 to investigate their potential role in disseminating pathogenic species in avocado orchards. Phytophthora multivora was isolated from one of the irrigation reservoirs, whereas, in two surveys conducted in nurseries, Phytophthora cinnamomi, the primary pathogen in avocado, was detected in addition to other Phytophthora species (P. lacustris, P. multivora, P. nicotianae, P. niederhauserii, and P. palmivora) and Phytopythium vexans. The species recovered from nurseries were isolated mostly from propagated plants but also from nursery irrigation water, soil used for substrate preparation, and soil samples collected in orchards that supply seeds for seedling propagation. Species recovered from nurseries correlated with those found in avocado orchards in a previous study, except for P. lacustris, suggesting that nurseries could be involved in their dissemination in avocado orchards. The improved sanitary status of nurseries resulted in reduced incidence in the second survey, indicating the importance of nursery monitoring to reduce infestations.

A semi-automated tool for identifying agricultural roadway crashes in crash narratives

Objective: Crash reports contain precoded structured data fields and a crash narrative that can be a source of rich information not included in the structured data. The narrative can be useful for identifying vulnerable roadway users, such as agricultural workers. However, using the narratives often requires manual reviews that are time consuming and costly. The objective of this research was to develop a simple and relatively inexpensive, semi-automated tool for screening crash narratives and expediting the process of identifying crashes with specific characteristics, such as agricultural crashes. Methods: Crash records for Louisiana from 2010 to 2015 were obtained from the Louisiana Department of Transportation (LaDOTD). Records with narratives were extracted and stratified by vehicle type. The majority of analyses focused on a vehicle type of farm equipment (Type T). Two keyword lists, an inclusion list and an exclusion list, were created based on the published literature, subject-matter experts, and findings from a pilot project. Next, a semi-automated tool was developed in Microsoft Excel to identify agricultural crashes. Lastly, the tool's performance was assessed using a gold standard set of agricultural narratives identified through manual review. Results: The tool reduced the search space (e.g., number of narratives that need manual review) for narratives requiring manual review from 6.7 to 59.4% depending on the research question. Sensitivity was high, with 96.1% of agricultural crash narratives being correctly classified. Of the gold standard agricultural narratives, 58.3% included an equipment keyword and 72.8% included a farm equipment brand. Conclusion: This article provides information on how crash narratives can supplement structured crash data. It also provides an easy-to-implement method to facilitate incorporating narratives into safety research along with keyword lists for identifying agricultural crashes.

Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production. Results of this study show that the consumed and output energies of sugarcane production are in average 172,856.14 MJ ha^-1, 120,000 MJ ha^-1 in planted farms and 122,801.15 MJ ha^-1, 98,850 MJ ha^-1 in ratoon farms, respectively. Results show that, in sugarcane production, electricity, machinery, biocides and sugarcane stem cuttings have the largest impact on the indices in planted farms. However, in ratoon farms, electricity, machinery, biocides and nitrogen fertilizers have the largest share in increasing the indices. ANN model with 9-10-5-11 and 7-9-6-11 structures are the best topologies for predicting environmental impacts and output energy of sugarcane production in planted and ratoon farms, respectively. Results from ANN models indicated that the coefficient of determination (R²) varies from 0.923 to 0.986 in planted farms and 0.942 to 0.982 in ratoon farms in training stage for environmental impacts and outpt energy. Results from ANFIS model, which is developed based on a hybrid learning algorithm, showed that, for prediction of environmental impacts, R² varies from 0.912 to 0.978 and 0.986 to 0.999 in plant and ratoon farms, respectively, and for prediction of output energy, R² varies from 0.944 and 0.996 in planted and ratoon farms. Results indicate that ANFIS model is a useful tool for prediction of environmental impacts and output energy of sugarcane production in planted and ratoon farms.

How low can you go? Estimating impacts of reduced pesticide use

Recent research has suggested that a high proportion of farms can dramatically reduce pesticide applications without reducing crop yields or farm profits. Yet this research has made estimation choices that may systematically bias downwards estimates of the productivity and profitability of pesticides. Fifty years of agricultural economics research provides lessons about how to avoid pitfalls in estimating pesticide productivity. Carefully executed econometric studies have found evidence of overuse, underuse, and near-optimal use of pesticides in different cropping systems. There are now standard methods to test and correct for sources of biases (either upward or downward) in estimates of pesticide productivity. Ignoring these lessons and methods can seriously bias estimates of the potential for reducing pesticide use at little or no economic cost. © 2018 Society of Chemical Industry.

The impact of soil erosion on internal migration in China

The impact of environmental change on internal migration has received wide attention in recent years. Mass internal migration has been a significant economic and social phenomenon in China, and soil erosion is a major environmental problem that impacts sustainable socioeconomic development. This study aims to identify the impact of soil erosion on internal migration in China at the county level based on gravity model by analyzing related data, such as the sixth national population census data and the soil and water conservation survey data. The results of spatial overlay could not identify an obvious relationship between soil erosion and net outmigration in China. The traditional gravity model of migration is modified to analyze the impact of soil erosion on net outmigration while other variables are controlled. The results indicate that only serious soil erosion increases the possibility of outmigration and that the impact is considerably higher in agricultural counties than in non-agricultural counties. In general, the impact of soil erosion on internal migration is far less than the impact of socioeconomic factors.

[Effects of different land use patterns on soil potassium distribution in Chengdu Plain, China]

To explore the effects of land use change on the potassium in soil profile under the background of rapid urbanization, we collected data of 187 soil profiles from four typical land use patterns (rice-wheat, rice-vegetable, rice-oil and garden) in Chengdu Plain. The contents of available potassium (AP), slow-acting potassium (SP), mineral potassium (MP), and total potassium (TP) in soil profile under different land use patterns and their relationships were analyzed. Our results showed that compared with the traditional rotation (rice-wheat, rice-oil), soil AP and SP contents significantly varied among different land use patterns. Rice-vegetable rotation increased the contents of AP and SP in the surface soil, while garden land increased the consumption of AP and SP in the soil. For the more stable forms, soil MP and TP, there was no significant difference in their contents under different land use patterns. In the deep soil, the content of AP in the rice-vegetable rotation pattern was significantly decreased with deepening soil layer, and the AP in traditional rotation was significantly higher than that in garden land. The trend of SP was opposite to that of AP. The difference of MP and TP in different land use patterns was small. Among the four land use patterns, the ratio of AP to TP and SP to TP in the lower layer of rice-vegetable rotation was higher than that in other patterns, while the ratio of AP to TP decreased significantly under different land use patterns at 20-40 cm. The change of SP to TP with the downward ratio of soil layer was opposite to that of AP to TP. Additionally, the ratio of MP to TP was relatively stable under different land use patterns. Therefore, different land use patterns exerted significant effects on the distribution of AP and SP in the soil profile of Chengdu Plain.

[The characteristics of soil water and gas transport under different land use patterns in the water-wind erosion crisscross region]

Understanding the effects of land use patterns on soil water and gas transport in the water-wind erosion crisscross region can provide guidance for high-efficiency use of limited soil and water resource in the ecological rehabilitation process on the Loess Plateau. To investigate the cha-racteristics of soil water and gas transport under different land use patterns and to study the relationships between soil saturated hydraulic conductivity (K_s), air permeability (K_a) and relative gas diffusivity (D_P/D₀), we collected soil samples (0-5 cm depth) from Caragana korshinskii land, abandoned land, alfalfa land, cropland, and bare land. K_s was measured by constant-head method. D_P/D₀ was measured by gas chamber method. K_a under field capacity (FC) was measured using the soil gas permeability meter. Results showed that soil bulk density (ρ_b) ranked as alfalfa land>bare land>abandoned land>C. korshinskii land >cropland, with that of abandoned land, bare land and alfalfa land being significantly different from that of cropland. Total soil porosity (Φ) ranked as cropland>C. korshinskii land>abandoned land>bare land>alfalfa land. Compared with cropland, Φ of alfalfa land, bare land and abandoned land was lower by 7.5%, 4.7% and 3.1%, respectively. Air filled porosity (ε₁₀₀) ranked as cropland>abandoned land>C. korshinskii land>bare land>alfalfa land. ε₁₀₀ of alfalfa land, bare land, C. korshinskii land and abandoned land was lower by 38.3%, 33.6%, 12.8% and 10.1%, respectively, as compared with cropland. Soil K_s ranked as abandoned land>C. korshinskii land>alfalfa land>bare land>cropland, with that of the abandoned land being significantly higher than the other four land use patterns. Soil K_a ranked as abandoned land>alfalfa land>C. korshinskii land>bare land>cropland, with that of abandoned land being significantly diffe-rent with cropland. Soil D_P/D₀ ranked as abandoned land> C. korshinskii land> alfalfa land>cropland>bare land, in which D_P/D₀ of C. korshinskii land and abandoned land was significantly higher than cropland by 36.8% and 61.6%, respectively. There were significant correlations between K_s and K_a, D_P/D₀ under FC conditions. Land use patterns significantly changed soil permeability. Farmland, abandonment, C. korshinskii, and alfalfa plantation improved hydraulic and gas transport parameters of the surface soil. In contrast, farmland and bare land had poor capability of soil water and gas transport.

Intensive tobacco cultivations, a possible public health risk?

The cultivation of tobacco has serious consequences for the environment: it impoverishes the soil by assimilating its nutrients, it involves an intensive use of highly polluting pesticides, it perturbs the ecosystem through deforestation, and it releases nicotine into the environment, which is toxic for humans. Italy is the first producer of raw tobacco in Europe and the Valtiberina area is among the most profitable. The first cultivations can be reconducted to the period around 1400. The objective of this experimental work is to verify the sustainability of tobacco cultivation near other crops using nicotine as an indicator. The nicotine on medicinal and wild plants adjacent to tobacco crops has been analyzed, assessing whether it is present or not and which is the concentration. To measure the nicotine present with ultra-high-performance liquid chromatography (UHPLC), LC/MS (liquid chromatography-mass spectrometry) method was used with LOQ (quantification limit) of 0.005 mg/kg. A total of 300 lots of aromatic herbs were sampled, and nicotine was detected in 82.3% of the samples in 2015 and 62.9% in 2016. Furthermore, in 2015, 121 samples of wild material were analyzed, of which 88.4% showed traces of nicotine. These first results indicate a possible potential threat for the population health. This shows that the tobacco cultivation should not be in close proximity to other plantation destined for nutrition, neither for man and nor animals. The elevated impact of nicotine on the ecosystem has negative consequences not only for the economy but it is also a potential public health threat.

Effects of straw returning and feeding on greenhouse gas emissions from integrated rice-crayfish farming in Jianghan Plain, China

Great efforts have been devoted to assessing the effects of straw managements on greenhouse gas (GHG) emissions, global warming potential (GWP), and net economic budget in rice monoculture (RM). However, few studies have evaluated the effects of straw managements on GHG emissions and net ecosystem economic budget (NEEB) in integrated rice-crayfish farming (RC). Here, a randomized block field experiment was performed to comprehensively evaluate the effects of aquatic breeding practices (feeding or no feeding of forage) and straw managements (rice straw returning or removal) on soil NH₄⁺-N and NO^-₃-N contents, redox potential (Eh), CH₄ and N₂O emissions, GWP, and NEEB of fluvo-aquic paddy soil in a rice-crayfish co-culture system in Jianghan Plain of China. We also compared the differences in CH₄ and N₂O emissions, GWP, and NEEB between RM and RC. Straw returning significantly increased CH₄ and N₂O emissions by 34.9-46.1% and 6.2-23.1% respectively compared with straw removal. Feeding of forage decreased CH₄ emissions by 13.9-18.7% but enhanced N₂O emissions by 24.4-33.2% relative to no feeding. Compared with RM treatment, RC treatment decreased CH₄ emissions by 18.1-19.6% but increased N₂O emissions by 16.8-21.0%. Moreover, RC treatment decreased GWP by 16.8-22.0% while increased NEEB by 26.9-75.6% relative to RM treatment, suggesting that the RC model may be a promising option for mitigating GWP and increasing economic benefits of paddy fields. However, the RC model resulted in a lower grain yield compared with the RM model, indicating that more efforts are needed to simultaneously increase grain yield and NEEB and decrease GWP under RC model.

Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: population based case-control study

OBJECTIVE

To examine associations between early developmental exposure to ambient pesticides and autism spectrum disorder.

DESIGN

Population based case-control study.

SETTING

California's main agricultural region, Central Valley, using 1998-2010 birth data from the Office of Vital Statistics.

POPULATION

2961 individuals with a diagnosis of autism spectrum disorder based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, revised (up to 31 December 2013), including 445 with intellectual disability comorbidity, were identified through records maintained at the California Department of Developmental Services and linked to their birth records. Controls derived from birth records were matched to cases 10:1 by sex and birth year.

EXPOSURE

Data from California state mandated Pesticide Use Reporting were integrated into a geographic information system tool to estimate prenatal and infant exposures to pesticides (measured as pounds of pesticides applied per acre/month within 2000 m from the maternal residence). 11 high use pesticides were selected for examination a priori according to previous evidence of neurodevelopmental toxicity in vivo or in vitro (exposure defined as ever v never for each pesticide during specific developmental periods).

MAIN OUTCOME MEASURE

Odds ratios and 95% confidence intervals using multivariable logistic regression were used to assess associations between pesticide exposure and autism spectrum disorder (with or without intellectual disabilities) in offspring, adjusting for confounders.

RESULTS

Risk of autism spectrum disorder was associated with prenatal exposure to glyphosate (odds ratio 1.16, 95% confidence interval 1.06 to 1.27), chlorpyrifos (1.13, 1.05 to 1.23), diazinon (1.11, 1.01 to 1.21), malathion (1.11, 1.01 to 1.22), avermectin (1.12, 1.04 to 1.22), and permethrin (1.10, 1.01 to 1.20). For autism spectrum disorder with intellectual disability, estimated odds ratios were higher (by about 30%) for prenatal exposure to glyphosate (1.33, 1.05 to 1.69), chlorpyrifos (1.27, 1.04 to 1.56), diazinon (1.41, 1.15 to 1.73), permethrin (1.46, 1.20 to 1.78), methyl bromide (1.33, 1.07 to 1.64), and myclobutanil (1.32, 1.09 to 1.60); exposure in the first year of life increased the odds for the disorder with comorbid intellectual disability by up to 50% for some pesticide substances.

CONCLUSION

Findings suggest that an offspring's risk of autism spectrum disorder increases following prenatal exposure to ambient pesticides within 2000 m of their mother's residence during pregnancy, compared with offspring of women from the same agricultural region without such exposure. Infant exposure could further increase risks for autism spectrum disorder with comorbid intellectual disability.

Food security and food self-sufficiency around the world: A typology of countries

The particularities of agriculture, as a sector which ensures food supply, result from many factors, including the multilateral interaction between the environment and human activity. The extent of human intervention in the food production process is usually measured with the amount of capital expenditure. Therefore, the food production potential and the resulting food security depend on both natural and economic factors. This paper identifies the current status of food security in different countries around the world, considering both aspects (physical and economic availability) combined together. The variables published by FAO were used together with a variable estimated based on the author’s own methodology to identify 8 groups of countries characterized by economic development level, net trade in agricultural products, and selected variables related to agriculture and food situation. As shown by this study, the degree to which food security is ensured with domestic supply varies strongly across the globe. Domestic production provides a foundation for food security in wealthy countries, usually located in areas with favorable conditions for agriculture (including North America, Australia, New Zealand, Kazakhstan) and in countries which, though characterized by a relatively small area of arable land per capita, demonstrate high production intensity (mainly European countries). International trade largely contributes to food security in Middle East and North African countries as well as in selected South American countries which are net importers of food products. The most problematic food situation continues to affect Sub-Saharan Africa and Central Asia.

Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets

This paper highlights the importance of optimized shape index for agricultural management system analysis that utilizes the contiguous bands of hyperspectral data to define the gradient of the spectral curve and improve image classification accuracy. Currently, a number of machine learning methods would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three new optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of optimized shape indices, specifically the Moment Distance Ratio Right/Left (MDRRL), to discriminate between types of tillage (corn-min and corn-notill) and between grass/pasture and grass/trees, tree and grass under object-based random forest approach.

Assessing the socio-economic impact of climate change on wheat production in Khyber Pakhtunkhwa, Pakistan

This study involves the climate change impact assessment of wheat producers in Khyber Pakhtunkhwa, Pakistan. An extensive farm survey of 150 farms was designed. From study area, three districts, namely, Chitral, D.I. Khan, and Peshawar, were selected through multistage sampling process. Yield simulation from Crop model DSSAT (Decision Support System for Agro Technology Transfer) was used for socio-economic impact assessment. Future climate scenarios were generated by selecting five GCMs from latest CMIP5 family with two RCPs 4.5 and 8.5, at two carbon concentrations of 499 ppm and 571 ppm, respectively. Yield simulations were analyzed for each GCM. Results of crop model revealed that wheat yield will increase in district Chitral, while in D.I. Khan and Peshawar, yields would be reduced due to climate change. For socio-economic impact assessment, TOA-MD (Trade-Off Analysis for Multi-Dimensional Impact Assessment) version 6 was used. Climate change impacts on poverty, net farm returns, and per capita income were calculated for different scenarios. The analysis was carried out on per-farm basis. The economic model results revealed that climate change has negative impact on wheat producers in D.I. Khan and Peshawar while making wheat producers better off in Chitral. The number of losers ranged from 54 to 66.21% and 50 to 61.99% in D.I. Khan and Peshawar, respectively. Losers are the farmers who would be economically worse off under perturbed climate. With current climate, the observed poverty rate would be 34 to 49 in D.I. Khan while 21.26 to 34.03 in Peshawar. The study recommended need for adaptation strategies to overcome the vulnerabilities of climate change.

The dynamic nexus of crop production and population growth: housing market sustainability pathway

The urban poor residents in South Africa are over time known for imbalance and inadequate housing amidst recent concern of shock in food production. In studying this peculiar problem, this study investigates the cointegration and long-run equilibrium relationship of population growth, crop production, and the housing price in the country. Empirically, a quarterly data from 1975:Q1 to 2015:Q4 is employed using the conventional Autoregressive Distributed Lag. The investigation shows strong significant evidence of cointegration and a quarterly speed of adjustment of 17.2% to long run in the system. Also, as the population grows, a decline in house price index is experienced in the long run. Although unusual, adequate and sustainable housing plan, demand-supply dynamics, in respect to a country's population expansion could posit observation. But, in the short run, a strongly significant positive association is observed. It shows further that positively short-run and long-run relationships significantly exist between crop production and house price index. In reality, caution is essential in the introduction of land redistribution policy to avoid hampering the housing policies and 2030 housing target of the government.

Sustainable palm oil may not be so sustainable

The globalization of the palm oil trade poses a menace to the ecosystem integrity of Southeast Asia. In this short communication, we briefly discuss why palm oil certifications may have failed as an effective means to halt forest degradation and biodiversity loss. From a comparison of multiple new datasets, we analysed recent tree loss in Indonesia, Malaysia, and Papua New Guinea, and discovered that, from 2001 to 2016, about 40% of the area located in certified concessions suffered from habitat degradation, deforestation, fires, or other tree damages. Certified concessions have been subject to more tree removals than non-certified ones. We also detect significant tree loss before and after the start of certification schemes. Beyond non-governmental organisations' concern that Roundtable on Sustainable Palm Oil (RSPO) and Palm Oil Innovation Group (POIG) certifications allow ongoing clearance of any forest not identified as of high conservation values (HCV) or high carbon stock (HCS), we suggest an alarming and previously overlooked situation, such as that current "sustainable palm oil" is often associated with recent habitat degradation and forest loss. In other words, certified palm oil production may not be so sustainable.

High highs and low lows: Elucidating striking seasonal variability in pesticide use and its environmental implications

Despite substantial public and scientific concern regarding unintended environmental and health consequences of agricultural pesticide use, identifying when and where high levels of use occur is stymied by a dearth of data at biologically relevant spatial or temporal scales. Here we investigate intra-annual patterns in pesticide use by crop and by pesticide type using unique pesticide use data from agriculturally diverse croplands of California, USA. We find that timing and type of pesticide use is strongly crop-dependent, and that for many high pesticide use crops, monthly application rates are highly consistent from year-to-year. Further, while pesticide use hotspots are concentrated in early summer, regions with very high use occur throughout the year with spatial distributions varying therein. The enormity of intra-annual variation in pesticide use, as well as the consistency in those patterns through time, suggests opportunities for crop-specific pest management and region-specific mitigation approaches to limit environmental and human health hazards from agricultural pesticide use.

Impacts of rising temperature, carbon dioxide concentration and sea level on wheat production in North Nile delta

Climate change poses a serious threat to arid and low elevation coastal zones. Kafrelsheikh governorate, as a large agricultural and coastal region on the Egyptian North Nile Delta, is one of the most vulnerable areas to higher temperature and global sea level rise. Two DSSAT wheat models (CERES and N-Wheat) were calibrated using a local cultivar (Misr3) grown under irrigated conditions in Egypt. Experimental data of two successive growing seasons during 2014/2015 and 2015/2016 were used for calibration using different treatments of irrigation, planting dates and fertilization. Both models simulated the phenology and wheat yield well, with root mean square deviation of <10%, and d-index > 0.80. Climate change sensitivity analysis showed that rising temperature by 1 °C to 4 °C decreased wheat yield by 17.6%. However, elevated atmospheric CO₂ concentrations increased yield and could overtake some of the negative temperature responses. Sea level rise by 2.0 m will reduce the extent of agricultural land on the North Nile Delta of Egypt by ~60% creating an additional challenge to wheat production in this region.

Impacts of climatic variability on agriculture and options for adaptation in the Surma River basin, Bangladesh

The objectives of this study are to explore the impacts of climatic variability on agriculture and to find out the viable options for adaptation to the changing climate. The primary data are collected through a questionnaire survey, key informant interviews and field observations in two agriculture-based villages of the Surma River basin. A household-level structured questionnaire survey was conducted on 260 households selected from 746 through simple random sampling. The study first explored the impacts of climatic variability on agriculture and fishery. Then, the existing adaptation measures undertaken by the farmer to combat the adverse impacts of climatic variability were identified. It was found that climatic variability adversely affects the crop production, crop diversity and cropping pattern in the study area. It hampers production rate, cultivation area, soil productivity and the mode of irrigation of an agricultural system of an area. Similarly, inauspicious climatic events also destroy the fishery and livestock sectors. In addition, the socioeconomic circumstances of farmers are also being influenced by climatic change. Hence, farmers are adopting feasible adaptation measures to cope with and adapt to the adverse and changing climatic events. The present study explored a number of indigenous and modern adaptation measures undertaken by the farmers.

Socioecologically informed use of remote sensing data to predict rural household poverty

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.

Comparative on plant stoichiometry response to agricultural non-point source pollution in different types of ecological ditches

Long-term agricultural development has led to agricultural non-point source (NPS) pollution. Ecological ditches (eco-ditch), as specific wetland systems, can be used to manage agricultural NPS water and achieve both ecological and environmental benefits. In order to understand which type of eco-ditch systems (Es, soil eco-ditch; Ec, concrete eco-ditch; Eh, concrete eco-ditch with holes on double-sided wall) is more suitable for plant nutrient balance meanwhile reducing NPS water (total nitrogen [TN], about 10 mg/L; total phosphorus [TP], about 1 mg/L), it is essential to evaluate the plant (Vallisneria natans) stoichiometry response to water in different types of eco-ditches under static experiment. The results indicated that there were no significant differences in TP removal efficiency among three eco-ditches, yet Eh systems had the best TN removal efficiency during the earlier experimental time. Addition of agricultural NPS water had varying effects on plants living in different types of eco-ditch systems. Plant organ stoichiometry of V. natans varied in relation to eco-ditch types. Plant stoichiometry (C:N, C:P, and N:P) of V. natans in Eh systems could maintain the homeostasis of nutrients and was not greatly affected by external changing environment. V. natans in Es systems can more easily modify the nutrient contents of organs with regard to nutrient availability in the environment. Our findings provide useful plant stoichiometry information for ecologists studying other specific ecosystems.

Decreasing farm number benefits the mitigation of agricultural non-point source pollution in China

Agricultural non-point source pollution causes global warming and the deterioration of air and water quality. It is difficult to identify and monitor the emission sources of agricultural pollution due to the large number of farms in China. Many studies focus on the technological aspect of achieving agricultural sustainability, but its socioeconomic aspect is poorly understood. Here, we report how group size (number of farms in a certain region) affects agricultural pollution governance through conducting a social science experiment. We found that when communication was allowed among group members, a small group size facilitated cooperation. Although deviations from the cooperation equilibrium occurred with time in all groups, the smaller the group size, the slower the cooperation equilibrium became frangible. These findings suggest that reducing number of farms and extending the length of farm property rights can benefit the mitigation of agricultural non-point pollution in China. Social science experiments can be a useful tool to understand the socioeconomic aspect of agricultural sustainability.

Not one Brexit: How local context and social processes influence policy analysis

This paper develops an empirical agent-based model to assess the impacts of Brexit on Scottish cattle farms. We first identify several trends and processes among Scottish cattle farms that were ongoing before Brexit: the lack of succession, the rise of leisure farming, the trend to diversify and industrialise, and, finally, the phenomenon of the "disappearing middle", characterised by the decline of medium-sized farms and the polarization of farm sizes. We then study the potential impact of Brexit amid the local context and those ongoing social processes. We find that the impact of Brexit is indeed subject to pre-Brexit conditions. For example, whether industrialization is present locally can significantly alter the impact of Brexit. The impact of Brexit also varies by location: we find a clear divide between constituencies in the north (highland and islands), the middle (the central belt) and the south. Finally, we argue that policy analysis of Brexit should consider the heterogeneous social context and the complex social processes under which Brexit occurs. Rather than fitting the world into simple system models and ignoring the evidence when it does not fit, we need to develop policy analysis frameworks that can incorporate real world complexities, so that we can assess the impacts of major events and policy changes in a more meaningful way.

Reduction of wheat photosynthesis by fine particulate (PM2.5) pollution over the North China Plain

The negative impact of rapid urbanization in developing countries has led to a deterioration of urban and regional air quality. Much attention has been given to the impact of fine particulate pollution on urban public health. However, very little attention has been given to its impact on the regional ecosystem such as the agricultural ecosystem. Thus, we evaluate the direct impact of air pollution on the reduction of wheat photosynthesis by fine particulate matter (PM_2.5) pollution in the world's most heavily polluted area, the North China Plain, using remote sensing observations and ground measurements. We found the following to be true: (1) Heavy PM_2.5 pollution could significantly reduce wheat photosynthesis and cause an expositional relationship between the PM_2.5 concentration and wheat photosynthesis (R² = 0.9824, P < 0.05); (2) Heavy PM_2.5 pollution makes up 2% for the reduction in wheat photosynthesis at all wheat-plant farmlands in the North China Plain, approximately covering an area of 354,400 km²; (3) Increasing heavy PM_2.5 pollution significantly reduced wheat photosynthesis by 87% in wheat-planted farmland during 1999-2011. We hope the results presented here could draw attention to the effect of PM_2.5 pollution on the agricultural ecosystem and encourage further studies to evaluate the feedback of atmospheric pollution on the agricultural ecosystem using remote sensing. Abbreviation: Northern China Plain (NCP); normalized difference vegetation index (NDVI); The Moderate Resolution Imaging Spectroradiometer (MODIS); fine particulate matter (PM_2.5).