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In this study, we aim to determine the median lethal dose (LD 50 ) of glyphosate isopropylamine salt (GI), which is commonly used in the world and especially in Turkey against to weeds, in male and female rats by using the probit or logit analysis method. A total of 140 Wistar rats were used, including 70 females and 70 males. To determine LD 50 , the male and female rats were randomized into 7 groups made up of 10 animals in each group. At doses of 6000, 6500, 7000, 7500, 8000, 8500, and 9000 mg / kg, GI was administered to the male and female rats by oral gavage. After dosing, the animals were periodically monitored for 14 days. No deaths were observed after 48 h of herbicide application. In this study, only logit analysis was used for the LD 50 value to be calculated in the male rats within 24 h, while other analyses were carried out with the probit method. In the female and male rats, the LD 50 levels of GI between 24 and 48 h were determined as 7444.26–7878.50 mg/kg and 7203.58–7397.25 mg/kg, respectively. According to these results, it was concluded that female rats are more sensitive to GI than male rats. We believe that the findings that were obtained will guide researchers, clinicians, and toxicologists through preventive and curative studies against acute poisoning that may occur with GI.

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effect models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that (1) circumvent the need for analytic approximations, numerical integration, or Metropolis–Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Pólya–Gamma distribution, are implemented in the R package BayesLogit . Supplementary materials for this article are available online.

Climate change is one of the multiple challenges facing all categories of farmers globally. However, African farmers are the most sensitive in respect of climate variability and change. Climate change impacted negatively on crop production and the livelihoods of the local farmers. In black township South Africa, agricultural activities are highly dominated by small-scale farmers, whose farming system is highly vulnerable to changes in climate. This paper presents the analysis of how small-scale farmers employed adaptation strategies in response to climate change and determinants of small-scale households’ choices of coping and adaptation approach to climate variability and change in Vhembe District, South Africa. Multi nominal logit model was used on a surveyed of 224 local farmers. Farmers’ socio-economic attributes, was used in response to climate changes and further, households have adaptation strategies both on-farm and off-farm approach. Such approaches were; drought-tolerant seeds, shorter cycle crops, diversification of crops, changing planting dates, small-scale irrigation, migrating to urban areas and involvement in petty business. The results of the findings reveals that the significant drivers affecting choice of adaptation approach include climate information, gender, farm size, education level, farmer experience, decreasing rainfall and increases in temperature as farmers’ determinant choices of adaptation to climate change. While, on the other hand, off-farm resources, headed households and age had no significant impact on the choice of coping and adaptation approach to climate change. Therefore, policy makers in the local municipality should play a significant role by enhancing adaptation strategies appropriate for particular climatic shock on the study area.

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

ContextAround 30% of European agricultural landscapes are classified as high nature value (HNV) farmlands. Current policies emphasize the multifunctionality of these landscapes, but little is known about the positive and negative associations of multiple ecosystem services within HNV farmland.ObjectivesThis study aims to identify perceived ecosystem services synergies, trade-offs, and bundles in agricultural landscapes of HNV from a socio-cultural perspective.MethodsWe performed a participatory mapping survey of 10 ecosystem services categories among 2301 rural residents in 13 European sites. We analyzed bivariate synergies and trade-offs between perceived ecosystem services through nonparametric correlation analyses. Spatial bundles of perceived ecosystem services were identified through hierarchical cluster analysis. Multinomial logit models were used to assess the influence of land cover on generating associations of ecosystem services.ResultsWe find two strong and 16 moderate synergies of perceived ecosystem services (out of 46 possible ecosystem services pairs), mainly among different cultural ecosystem services. We do not reveal moderate or strong trade-offs. We identify five spatial bundles of ecosystem services, termed “Ecosystem services coldspots”, “Wild harvesting ranges”, “Nature areas”, “Recreational spaces”, and “Ecosystem services hotspots”. Of all land-cover co-variates, natural areas, urban areas, and roads have the strongest explanatory power.ConclusionsOur study complements prevailing biophysical and economic analyses of ecosystem services synergies, trade-offs and bundles by a spatially explicit, socio-cultural perspective. We conclude that socio-cultural mapping of ecosystem services is useful for understanding the perceived multifunctionality of a landscape.

Smartphone technology is promising for the future development of agriculture, as it can facilitate and improve many operational procedures and can also be combined with precision agriculture technologies. Yet, existing research on smartphone adoption in agriculture is scarce. Therefore, this paper empirically explores the factors influencing smartphone adoption by German farmers. The relationship between farmers, farm characteristics and smartphone adoption was analysed using a binomial logit model. The dataset, collected in 2016, consists of 817 German farmers and is representative in terms of age, farm size and diversification as well as regional distribution across the study area. The results indicate that, among other factors, farmers’ age, education, and farm size are determinants of smartphone adoption. Furthermore, the paper provides descriptive information about the usage of smartphone functions and agriculture-related app functions. Thus, this paper contributes to the literature by identifying key determinants of smartphone adoption in agriculture. The findings may be of interest for policy makers, researchers in the field of precision agriculture technologies as well as developers and providers of farm equipment and precision agriculture technologies that integrate with smartphones, since the paper includes information concerning smartphone use and key factors influencing smartphone adoption.

Background The agricultural sector remains the main source of livelihoods for rural communities in Ethiopia, but faces the challenge of changing climate. This study investigated how smallholder farmers perceive climate change, what adaptation strategies they practice, and factors that influence their adaptation decisions. Both primary and secondary data were used for the study, and a multinomial logit model was employed to identify the factors that shape smallholder farmers’ adaptation strategies. Results The results show that 90% of farmers have already perceived climate variability, and 85% made attempts to adapt using practices like crop diversification, planting date adjustment, soil and water conservation and management, increasing the intensity of input use, integrating crop with livestock, and tree planting. The econometric model indicated that education, family size, gender, age, livestock ownership, farming experience, frequency of contact with extension agents, farm size, access to market, access to climate information and income were the key factors determining farmers’ choice of adaptation practice. Conclusion In the Central Rift Valley of Ethiopia, climate change is a pressing problem, which is beyond the capacity of smallholders to respond to autonomously. Farmers’ capacity to choose effective adaptation options is influenced by household demography, as well as positively by farm size, income, access to markets, access to climate information and extension, and livestock production. This implies the need to support the indigenous adaptation strategies of the smallholder farmers with a wide range of institutional, policy, and technology support; some of it targeted on smaller, poorer or female-headed households. Moreover, creating opportunities for non-farm income sources is important as this helps farmers to engage in those activities that are less sensitive to climate change. Furthermore, providing climate change information, extension services, and creating access to markets are crucial.

Background Livelihood diversification plays a decisive role for the reduction of poverty, food insecurity and to improve the welfare of rural communities. However, inadequate research attention has been given to explore the determinants of livelihood diversification strategies in resettlement areas of Ethiopia. This study attempts to investigate determinants of livelihood diversification strategies among the resettler households in Chewaka district of Ethiopia. Methods The study utilized both primary and secondary data which are qualitative and quantitative in their nature. Through multistage sampling procedure, a total of 384 households were selected from seven sample kebeles of Chewaka district. Data were collected using interview schedule, focus group discussions and field observations. The collected data were analyzed quantitatively and qualitatively. Descriptive and inferential statistics along with multinomial logit model have been employed to analyze the data. Results The results showed that agriculture (43.2%), agriculture plus non-farm (25.5%), agriculture plus off-farm (19.3%) and a combination of agriculture plus non-farm plus off-farm (12%) activities are the most pertinent livelihood strategies in the study area. It was found that agriculture has a leading contribution to the total households’ income (72.5%) followed by non-farm (20%) and off-farm activities (7.5%). Multinomial logit model result revealed that land holding size, educational status, livestock holding, sex, age, market distance, credit access, annual income, access to training and household sizes were the major determinants of livelihood diversification strategies. Moreover, poor infrastructural development, lack of working capital, absence of technical support, inadequate skill training and lack of awareness are constraints to livelihood diversification in the area. Conclusions The study concludes that agricultural sector alone cannot be relied upon as the core activity for rural households and as a means of reducing poverty, achieving food security and improving livelihoods in the study area. Thus, a comprehensive development plan that enhances successful livelihood diversification is found to be imperative and most urgent. Policies and actions directed towards improving livelihood of the resettlers’ communities should focus on expanding rural infrastructures, enhancing awareness creation activities and cooperation of stakeholders to bring sustainable livelihood outcome in the area.

The purpose of this study is to discover the factors shaping public opinion about renewable energy sources and investigate willingness to pay for expansion of renewable energy sources in the electricity mix. Data was collected through a questionnaire applied in Nikaia, an urban municipality of Greece. The respondents have a positive attitude towards renewable energy systems. Most of them have good knowledge of solar and wind energy systems and are using solar water heating, while several respondents own a solar PV system. Environmental protection is seen as the most important reason for investing in a renewable energy system. Willingness to pay for a wider penetration of RES into the electricity mix was estimated to be 26.5 euros per quarterly electricity bill. The statistical analysis revealed the existence of a relationship between RES perceived advantages and willingness to pay for renewable energy. Furthermore, by using a binary logit model, willingness to pay was found to be positively associated with education, energy subsidies, and state support.

People’s adaptive behaviour to increasing energy prices has been studied at length in transportation research. Prior research however has not addressed research questions concerning the contribution of travel-related changes in more encompassing energy conservation strategies. Moreover, context-dependency and choice under constraints has not been studies at any length. In this paper, we therefore report the results of a context-dependent elaboration of a mixture amount choice experiment to measure context-dependent responses to accumulative energy charges under budget constraints. Accounting for consumer heterogeneity in adaptive response behaviour, mixed logit analysis is used to analyse the extent and nature of adaptations of activity-travel behaviour and resource allocation in response to increasing energy costs. The results indicate that individuals are inclined to compensate for increased expenditures on energy due to price increases. Moreover, results show the existence of significant heterogeneity among respondents in terms of their adaptation strategies to various energy-saving choices.