Determinants of Farmers’ Decisions to Adopt Adaptation Technologies in Eastern Uganda

Using the Heckman sample selectivity model, this study identified farmers’ perception and adaptation to climate variability in Eastern Uganda, in order to support development of public policy and investment that can help increase adaptation to climate variability. The study was based on the premise that farmers who perceive change in climate and respond (or fail respond) share some common characteristics, which are important in understanding the reasons underlying their response (or failure to respond). Stratified random sampling was used to obtain a sample of 353 households across the three agro-ecological zones in Eastern Uganda, from which data was collected. In addition, 9 focus group discussions and 23 Key Informants Interviews were conducted, targeting smallholder farmers and agricultural stakeholders in the region. Results indicate that farmers’ decisions to adopt adaptation technologies are primarily determined by their perceptions of rainfall adequacy (subjective index). The probability of adoption of adaptation technologies by male headed households and those with more members showed a 12% and 23% higher chance of adaptation respectively as compared to their counterparts. These factors relate to labour endowment, implying the need to build strong social protection mechanisms at household and community levels.    The probability of responding to climate variability also varied by location with a 15% and 6% smaller chance for location in Mbale and Sironko respectively as compared to Pallisa. Access to weather information is the single most important factor affecting farmers’ perceptions of climate variability, implying the need to develop and dissemination appropriate weather information to guide farmers in making adaptation decisions.   Key words: Adaptation, Climate Variability, Eastern Ugand

RELATIONSHIP AMONG TYPE OF SCHOOL, ACADEMIC RESILIENCE AND ACADEMIC ACHIEVEMENT AMONG SECONDARY SCHOOL STUDENTS IN KIAMBU COUNTY, KENYA

This study investigated the relationship among type of school, academic resilience and academic achievement among secondary school students in Kiambu County, Kenya. A descriptive correlational design was used. A randomly selected sample of 390 students in the third year of secondary education was involved. The participants were drawn from four categories of secondary schools: Boys boarding, girls boarding, coeducational boarding and coeducational day. Data were collected using a demographic form and the California Healthy Kids Survey (CHKS) resilience scale. A significant mean difference (F (3,386) = 9.39, p < .05) was reported in the academic resilience scores when type of school was considered. The difference was in favour of girls’ boarding schools. The mean academic resilience and achievement for boys’ boarding was found to be significantly lower than that of girls’ boarding, boys’ day, and mixed day secondary schools.  It was recommended that educational stakeholders should shift from considering type of school as being peripheral to academic outcomes and instead regard it as a key contributor to the educational outcomes of secondary school students

Vertical and Horizontal Integration as Determinants of Market Channel Choice among Smallholder Dairy Farmers in Lower Central Kenya

This study sought to analyse vertical and horizontal integration as determinants of market channel among smallholder dairy farmers in Lower Central Kenya. Data was collected from 288 small holder dairy farmers in this region using multistage sampling technique. Processing and analysis of the survey data was carried out using SPSS version 20 and STATA version 12. Multinomial logit regression model (MNL) was used to analyse factors influencing the choice of dairy market outlet by the small holder dairy farmer. Level of education, milk output, access to information and transaction costs influenced the choice of marketing channel. Vertically integrated households used own outlet as marketing channel while horizontally integrated households used cooperative and farmers associations as milk marketing channel. It is recommended that programmes relating to milk market information be made accessible to farmers. There is need to profile farmers on the basis of production, spatial location and education level and encourage them to use specific marketing channel

Socio Economic Factors Influencing Utilization of Seasonal Climate Forecast Among Smallholder Farmers in Semi-Arid Lower Eastern Kenya: A Case of Masinga Sub-County

This paper discusses the influence of socio economic factors influencing utilization of Seasonal Climate Forecasts (SCF) by smallholder farmers in semi-arid lower Eastern Kenya in Masinga Sub County. Questionnaires were administered randomly to a total of 274 respondents in four administrative locations namely; Masinga Central Location, Musumaa Location, Musingini Location, and Katulye Location. Data on socioeconomic factors influencing utilization of climate forecast information was collected using questionnaires. Both descriptive and inferential statistics were used in data analysis and in particular, Pearson correlation was used to test the relationship between socioeconomic characteristic and utilization of SCF. Results established that there exist a positive relationship   between gender, age, education level, income, land size and utilization of seasonal climate forecast (p=.007,p=.000,p=.005,p=.000 and p=.003) respectively. The study concludes that socio economic factors cannot be ignored in dissemination of climate forecast information because they significantly affect utility. If these socio economic factors are observed in the entire process of climate forecasts production, and dissemination there is likelihood of increasing utility of climate forecasts by the households hence reaping benefits of forecasts. This study recommends that, socioeconomic factors be considered in the entire process of forecasts access and dissemination in order to reap benefits of the forecasts. This is because these factors have not been sufficiently prioritized as a fundamental instrument to enhance access and utilization of climate forecasts. Keywords: Lower Eastern Kenya, Perception, Seasonal Climate Forecast, Semi-arid, Smallholder farmers.

In-kind transfers of maize, commercialization and household consumption in Kenya

This article discusses in kind food transfers and whether such transfers should be interpreted as a sign of the failure of grain markets to meet the food demands of the poor. The paper elucidates on aspects of both consumption and in kind transfers of maize against a backdrop of poorly functioning markets. The paper adds to the theoretical understanding of household based linkages and provides a documentation of in kind commodity flows missing in many discussions of such linkages. The purpose of the paper is twofold: First, it sheds light on the phenomenon of in kind transfers of staple crops in the Kenyan context. Secondly, the article assesses the wider reciprocal and livelihood implications for the transferring households. The paper relies on three sets of data with respect to the methodology. It uses quantitative data collected at the household level in 2008, qualitative data collected at the village level in 2002 and 2008 as well as qualitative household level data gathered through in depth interviews with 30 heads of household and farm managers in Western Kenya in June and July of 2006. The survey found that 38% of the households transferred maize to their relatives. The explanations for in kind transfers are not primarily related to poor price incentives, but the functioning of household support systems across space. In kind transfers therefore at times drain the food resources of the sending households while constituting important sources of food security for receiving households. While the focus in the literature generally is on rural urban linkages, the direction of maize transfers was primarily rural to rural. The article concludes that existence of food transfers underpins the necessity of improving the commercial incentives for maize and other foodstuffs and eliminating the physical barriers to the free movement of foodstuffs across the national space

Evaluating Linear and Non-linear Dimensionality Reduction Approaches for Deep Learning-based Network Intrusion Detection Systems

Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ones. The technique aids reduce computational load, avoiding data over-fitting, and increasing model interpretability. Recent studies have revealed that dimensionality reduction can benefit from labeled information, through joint approximation of predictors and target variables from a low-rank representation. A multiplicity of linear and non-linear dimensionality reduction techniques are proposed in the literature contingent on the nature of the domain of interest. This paper presents an evaluation of the performance of a hybrid deep learning model using feature extraction techniques while being applied to a benchmark network intrusion detection dataset. We compare the performance of linear and non-linear feature extraction methods namely, the Principal Component Analysis and Isometric Feature Mapping respectively. The Principal Component Analysis is a non-parametric classical method normally used to extract a smaller representative dataset from high-dimensional data and classifies data that is linear in nature while preserving spatial characteristics. In contrast, Isometric Feature Mapping is a representative method in manifold learning that maps high-dimensional information into a lower feature space while endeavouring to maintain the neighborhood for each data point as well as the geodesic distances present among all pairs of data points. These two approaches were applied to the CICIDS 2017 network intrusion detection benchmark dataset to extract features. The extracted features were then utilized in the training of a hybrid deep learning-based intrusion detection model based on convolutional and a bidirection long short term memory architecture and the model performance results were compared. The empirical results demonstrated the dominance of the Principal Component Analysis as compared to Isometric Feature Mapping in improving the performance of the hybrid deep learning model in classifying network intrusions. The suggested model attained 96.97% and 96.81% in overall accuracy and F1-score, respectively, when the PCA method was used for dimensionality reduction. The hybrid model further achieved a detection rate of 97.91% whereas the false alarm rate was reduced to 0.012 with the discriminative features reduced to 48. Thus the model based on the principal component analysis extracted salient features that improved detection rate and reduced the false alarm rate

Network Intrusion Detection Systems: A Systematic Literature Review of Hybrid Deep Learning Approaches

Network Intrusion Detection Systems (NIDSs) have become standard security solutions that endeavours to discover unauthorized access to an organizational computer network by scrutinizing incoming and outgoing network traffic for signs of malicious activity. In recent years, deep learning based NIDSs have emerged as an active area of research in cybersecurity and several surveys have been done on these systems. Although a plethora of surveys exists covering this burgeoning body of research, there lacks in the literature an empirical analysis of the different hybrid deep learning models. This paper presents a review of hybrid deep learning models for network intrusion detection and pinpoints their characteristics which researchers and practitioners are exploiting to develop modern NIDSs. The paper first elucidates the concept of network intrusion detection systems. Secondly, the taxonomy of hybrid deep learning techniques employed in designing NIDSs is presented. Lastly, a survey of the hybrid deep learning based NIDS is presented. The study adopted the systematic literature review methodology, a formal and systematic procedure by conducting bibliographic review, while defining explicit protocols for obtaining information. The survey results suggest that hybrid deep learning-based models yield desirable performance compared to other deep learning algorithms. The results also indicate that optimization, empirical risk minimization and model complexity control are the most important characteristics in the design of hybrid deep learning-based models. Lastly, key issues in the literature exposed in the research survey are discussed and then propose several potential future directions for researchers and practitioners in the design of deep learning methods for network intrusion detectio

Empirical Evaluation of Adaptive Optimization on the Generalization Performance of Convolutional Neural Networks

Recently, deep learning based techniques have garnered significant interest and popularity in a variety of fields of research due to their effectiveness in search for an optimal solution given a finite amount of data. However, the optimization of these networks has become more challenging as neural networks become deeper and datasets growing larger. The choice of the algorithm to optimize a neural network is one of the most important steps in model design and training in order to obtain a model that will generalize well on new, previously unseen data. In deep learning, adaptive gradient optimization methods are mostly preferred for supervised and unsupervised tasks. First, they accelerate the training of neural networks and since mini batches are selected randomly and are independent, an unbiased estimate of the expected gradient can be computed. This paper examined six state-of-the-art adaptive gradient optimization algorithms, namely, AdaMax, AdaGrad, AdaDelta, RMSProp, Nadam, and Adam on the generalization performance of convolutional neural networks (CNN) architecture that are extensively used in computer vision tasks. Experiments were conducted giving comparative analysis on the behaviour of these algorithms during model training on three large image datasets, namely, Fashion-MNIST, Kaggle Flowers Recognition and Scene classification. The results show that Adam, Adadelta and Nadam finds the global minimum faster in the experiments, have a better convergence curve, and higher test set accuracy in experiments using the three datasets. These optimization approaches adaptively tune the learning rate based only on the recent gradients; thus, controlling the reliance of the update on the past few gradients

SSH-Brute Force Attack Detection Model based on Deep Learning

The rising number of malicious threats on computer networks and Internet services owing to a large number of attacks makes the network security be at incessant risk. One of the predominant network attacks that poses distressing threats to networks security are the brute force attacks. A brute force attack uses a trial and error algorithm to decode encrypted data such as passwords or Data Encryption Standard keys, through exhaustive effort (using brute force) rather than using intellectual strategies. Brute force attacks resemble legitimate network traffic, making it difficult to defend an organization that rely mainly on perimeter-based security solutions a major challenge. For stopping the occurrence of such attacks, several curable steps must be taken. This paper proposes an efficient mechanism for SSH-Brute force network attacks detection based on a supervised deep learning algorithm, Convolutional Neural Network. The model performance was compared with experimental results from 5 classical machine learning algorithms including Naive Bayes, Logistic Regression, Decision Tree, k-Nearest Neighbour, and Support Vector Machine. Four standard metrics namely, Accuracy, Precision, Recall, and the F-measure were used. Results show that the CNN-based model is superior to the traditional machine learning methods with 94.3% accuracy, a precision rate of 92.5%, recall rate of 97.8% and F1-score of 91.8% in terms of the ability to detect SSH-Brute force attacks.

Testing the central market hypothesis for food markets in the highlands of Central Kenya

Following extensive market liberalisation efforts in many developing countries, interest in food markets has grown tremendously. With the increase in participation of small traders to replace government controlled parastatals, it is important to assess whether liberalization policies have enhanced the efficiency of food markets. Maize is the main staple food in Kenya while beans are the most important pulse. An error correction model was used to test for bivariate causality between markets and examine the occurrence of central markets. The study used monthly retail prices of maize and beans in nine markets for a period of 15 years. The data was compiled from the sub counties ministry of agriculture annual reports. The results reveal the existence of central markets in the highlands of central Kenya. This shows a tendency of a more organised marketing system which is an indicator of market efficiency. The prices are determined in the low production zones meaning that demand markets are important in price formation. The central markets can be used by the government to effect desired policy changes especially price stabilization