In order to assess the nature of climate change, it is important to analyze the indicators of climate variability in different scales: spatial and temporal. The analysis at different scales can lead to understanding of the nature of variations. Climate change studies are essential for comprehending the nature of global processes, to refine global climate patterns and also develop further research for natural processes (Meinke, Stone, 2005; Hulme et al., 1999). Processes in nature are united, continuous and in constant interaction. Variance of interaction types are immeasurable, types can be connected with different scales and science fields, for example, biological, ecological, physical etc. If interaction is taking place between the land and atmosphere, it is defined as hydrological interaction. As water is significantly important for many purposes on the Earth, it is relevant to analyze precipitation and water runoff on a local scale. In the territory of Latvia, the amount of precipitation exceeds the level of evapotranspiration. Long-term monitoring data show that precipitation leads to average runoff of 250 mm per year (Ziverts, 2004). The monitoring data collected at three research sites located in Latvia was used for this research including Berze (Lielupe river basin, meteorological station in Dobele), Mellupite (Venta river basin, meteorological station in Saldus monitoring) and Vienziemite (Gauja river basin, meteorological station in Zoseni). The results from this study show that there is a pronounced interaction between runoff and precipitation with an average of 53 to 82%.

The goal of this research is to explore sentence structures expressed by parts of speech. Due to a small amount of data, a problem of sparse data has arisen, which was solved by recording the annotated sentences and considering a “framework” of a sentence made up from a verb and a noun, which was conditionally called a code. The code of a sentence is created by changing each word of a sentence by a symbol (letter or number) that encodes one or other property of that word as a constituent of the sentence. Zipf’s law describes sentences, encoded like that, rather well. If we ‘learn’ well to identify and analyze (annotate, translate, etc.) sentences of the simplest structure, we can automatically process quite a large part of text sentences. It is possible to identify at least 17% of sentences consisting of the simplest structure.

In rural Kazakhstan, the credit and insurance services are limited and the state support is weak. Therefore, households’ saving is crucial to provide an insurance against the economic and social shocks. The main goal of this study is to contribute to the literature on financial literacy in emerging economies, namely, the effect of financial literacy on saving rates of rural population. Being well educated not always means to be financial literate and make efficient decisions regarding one’s own finance. People with a lower formal education level but with better experience in consuming financial products could be better prepared for making financial decisions including those related to savings. In this paper other socio-economic determinants of saving rates were taken into account, such as an income level, family size and an employment status. This research was carried out in Pavlodar region of Kazakhstan, and the data collection took place in spring 2014. In total, 405 households were surveyed. Results of the analysis show that if a respondent gives at least one correct answer, it positively affects the saving rates as well as one can observe that the higher the financial literacy level, the higher are the saving rates. Availability of state supported financial education programs for rural people will significantly contribute to the financial literacy improvement. At the same time, providing various and appropriate financial products in rural areas will motivate rural people to search for new knowledge and require authorities to intensify activities in this field.

Climate change is an inevitable global challenge of the 21st century. For developing countries like Ethiopia, it intensifies existing challenges towards ensuring sustainable development. Adopting the protection motivation theory, this study examined factors affecting the practice of climate change adaptation and mitigation strategies among farmers. The study employed a mixed research approach to assess the subjective understanding of farmers about climate change threats and identify factors determining their responses to climate change effects. Qualitative data were collected using focus group discussions and interviews. Quantitative information was gathered using semi-structured survey from 296 randomly selected farmers. Qualitative data was dominantly analysed using content analysis, while descriptive and inferential statistics were applied to analyse quantitative data. Almost all respondents (97%) perceived that climate change was occurring and threatening their wellbeing. Dwindling precipitation, increasing temperature and occurrence of human and animal disease were perceived to represent climate change effects. From nationally initiated strategies, farmers were found to largely practice soil and water conservation, which they perceived as less costly and compatible to local knowledge. The result of binary logistic regression revealed that perceived severity of climate change, perceived susceptibility to climate change threat, perceived own ability to respond, response efficacy, and cost of practices predicted farmers’ motivation to practice climate change adaptation and mitigation strategies. Thus, building a resilient system should go beyond sensitizing climate response mechanisms. Policies should focus on human capital development and economic empowerment which would enable farmers to pursue context-specific adaptation and mitigation strategies, thereby maintaining a sustainable livelihood.

The technical inspection of a building carried out by an expert in civil engineering can identify and classify the physical conditions of the real estate; this generates relevant information for the protection and safety of users. Given the real conditions of the property, and for the real estate valuation universe, using artificial intelligence and fuzzy logic, it is possible to obtain the market price associated with the physical conditions of the building. The objective of this experiment is to develop a property evaluation model using a civil engineering inspection form associated with artificial intelligence, and fuzzy logic, and also compare with market value to verify the applicability of this inspection form. Therefore, the methodology used is based on technical inspection of civil engineering regarding the state of conservation of properties according to the model used in Portugal and adapted to the reality of Latvia. Artificial intelligence is applied after obtaining data from that report. From this, association rules are obtained, which are used in the diffuse logic to obtain the price of the apartment per square meter, and for comparison with the market value. For this purpose, 48 samples of residential apartments located in the city of Jelgava in Latvia are used, with an inspection carried out from October to December 2019. The main result is the 9% error metric, which demonstrates the possibility of applying the method proposed in this experiment. Thus, for each apartment sample consulted, it resulted in the state of conservation and a market value associated.

Poultry production decision setting is full of risk and imperfect information. Attitude towards risk is a measure of farmers’ willingness to take risks which is an important determinant in their production decisions. Strong social capital emanating from social networks can lead to efficient risk management strategies, thereby minimizing risks faced by the farmers. Therefore, the effects of social capital on the risk attitude of small-scale commercial poultry farmers in Oyo state were assessed. Data were collected from two hundred small-scale farmers and analysed using descriptive and inferential statistics, factorial analysis, multinomial logit and a two-stage least square. Results showed that 52.5%, 37.5% and 10% of the poultry farmers were risk averse, risk neutral and risk preferring, respectively. About 31.4% and 68.6% of the female and male farmers respectively were risk averse. Close to a fifth, a quarter and two-thirds of the risk averse, risk neutral and risk takers respectively contributed 21-30% of the decisions in the associations. Fourteen percent of the farmers belonged to homogeneous groups. The choice of being risk averse was affected by marital status, educational level, family size, percentage spent on poultry income and aggregate social capital. There was no reverse causality between risk attitude and social capital.

In rural Kazakhstan, the credit and insurance services are limited and the state support is weak. The main goal of this study is to contribute to the literature on financial literacy in emerging economies, namely, the effect of financial literacy on saving rates of rural population. Being well educated not always means to be financial literate and make efficient decisions regarding one’s own finance. People with a lower formal education level but with better experience in consuming financial products could be better prepared for making financial decisions including those related to savings. In this paper other socio-economic determinants of saving rates were taken into account, such as an income level, family size and an employment status. This research was carried out in Pavlodar region of Kazakhstan, and the data collection took place in spring 2014. In total, 405 households were surveyed. Results of the analysis show that if a respondent gives at least one correct answer, it positively affects the saving rates as well as one can observe that the higher the financial literacy level, the higher are the saving rates. Availability of state supported financial education programs for rural people will significantly contribute to the financial literacy improvement.

Nitrogen management strategy in plant growth period based on chlorophyll content evaluation in plant can improve nitrogen usage efficiency and reduce environmental contamination. This study is aimed to determine the impact of different nitrogen and sulphur fertilizers rates on dynamics of chlorophyll content in winter wheat during vegetative growth and to determine the relationship between nitrogen and chlorophyll content and grain yield of winter wheat. Field trial involving a winter wheat (Triticum aestivum L.) variety ‘Kranich’ was conducted at the LUA Research and Study Farm Vecauce during a three-year period (2012-2015). The treatments were 0, 85, 153, 175+S21, 175 (in 2015), 187 N kg haE−1 and different nitrogen norms according to chlorophyll meter Yara N-tester (Konica Minolta Ltd.) data: 180, 150, 205 N kg haE−1 depending on the year. The results of the trial show that the maximum chlorophyll content in different plant parts was observed at the end of flowering stage. The chlorophyll content depended on the level of mineral fertilisation. The highest chlorophyll content in leaves, stems and ears was obtained by using additional sulphur in two trial years. Usage of chlorophyll meter Yara N-tester obtained the highest chlorophyll content in all analysed plant parts in one trial year. Chlorophyll content was significantly dependant on plant growth stage in stems in all trial years, in leaves in two trial years, and in ears in one year. Nitrogen fertilization significantly affected chlorophyll content in leaves and stems in one trial year. Close positive correlation was observed between grain yield and wheat plant chlorophyll content and average nitrogen concentration at the end of flowering stage in all three trial years.

Latvian Blue (LZ) cows are kept for a variety of herd colours and because of national cow breed value. Resources of LZ cow breed remained low over the last decade. The aim of the study was to evaluate LZ cow breed linear conformation traits and analyse results in relation to their possession to the gene pool (GF) status and productive lifetime in 2017−2018. Information of the Agriculture Data Centre Republic of Latvia (LDC) and linear evaluation in vivo were used. The number of LZ cows registered in LDC in 2017−2018 was 1648 and only 339 cows were gene-pool animals (LZ GF), and 140 of LZ GF animals were culled because of different reasons in 2017−2018. There were registered 544 (190 primiparous of them) closed standard LZ cow lactations in 2009 and only 292 (64 primiparous of them) in 2018. The oldest LZ GF cow was 16.3 years old; 20% of LZ GF cows were more than 10 years old, and only 10% were younger than 5 years. The overall exterior rating was established more than 80 points for 64.4% of the evaluated LZ GF cows. Some LZ GF linear conformation traits did not change significantly (p is greater than 0.05) with age – exterior, milking properties and cow’s temperament. Udder parameters, rump and foot angle became worse with animals advancing in age and could be improved by breeding. No statistically significant differences (p is greater than 0.05) were established regarding all parameters of conformation traits among LZ and LZ GF dairy cows except a tendency of larger body depth in LZ GF (p=0.054). In conclusion, the linear conformation traits of LZ cows, despite the presence of genetic influence of other breeds, do not differ significantly from the gene pool animals. Rump angle, udder and teat qualities became worse with age in LZ and LZ GF cows. Legs and hoofs were stable parameters inherited by the LZ offspring.

Unemployment is a major issue in Nigeria because the youth finds it difficult or impossible to secure jobs that meet their expectations. There appears to be a mismatch between graduate skills and those sought by employers. The study investigated the effect of International Institute of Tropical Agriculture (IITA) youth Agripreneurs (IYA) programme on employment and income generation on cattle fattening enterprise under the N2Africa Borno youth project. A two-stage sampling procedure was used to select IYA participants and non-participants. Results revealed that the majority of respondents were males in both categories. It also indicated that youths who are in their productive age are more into cattle fattening enterprise in both categories. The study also found that, unemployment and skill acquisition are the main driving reasons for participating in IYA. It also indicated that both categories of respondents’ required external capital support for start-up of their enterprise, and that IYA programme participants on the average earn more income per month than nonparticipants. IYA programme participants generated more employment opportunities which implied that the programme had not only empowered participants but also made the participants’ become employers. The study recommended that there is a need to support youths with funds, access to trainings on capacity development and skill acquisition to make them self-employed and job creators.