Comparison of Water Use Savings and Crop Yields for Clay Pot and Furrow Irrigation Methods in Lake Bogoria, Kenya

As population grows mainly in developing countries resulting in an increase in water scarcity particularly in arid areas, irrigated agriculture is required to produce more food while using less water, and to do so without degrading the environment. The extent of improvement of water management in arid lands involves very high costs and irrigation methods that can help meet this challenge by giving growers greater control over the application of water is desirable. Clay pot is an efficient and cheap irrigation method that does not require water of high quality. Despite the significant efforts at Kapkuikui informal irrigation scheme to increase food production using furrow irrigation method, production has been declining over time due to water scarcity and fields abandoned as a result of salinity raising the need for improvement of the water productivity using an environmentally sound irrigation method. The objective of the present study was to evaluate water use savings under clay pot compared to furrow irrigation methods using field trials of maize and tomato crops and also soil water balance techniques. In addition, analysis of the salinity of irrigation water and soil at the scheme was done. Results indicate that the irrigation water sourced from springs at the scheme is saline with a salinity of 0.85g/l. The clay pot system was found to be more efficient than the furrow irrigation method by saving 97.1% of applied water for the maize crop and 97.8% for the tomato crop respectively. In terms of yield increases, the clay system was more productive per unit of water than the furrow irrigation method. The maize grain yields was 32.2% higher than that under the furrow, while fresh fruit tomato yields was 43.7% higher in the clay pot system than the furrow. Keywords: maize, tomato, water productivity, semi-arid.  

Socio-Economic and Institutional Constraints to Accessing Credit among Smallholder Farmers in Nyandarua District, Kenya

Amongst the challenges faced by smallholder farmers in production is inaccessibility to credit. This study sought to identify household socio-economic and institutional constraints influencing access to credit among smallholder farmers in Nyandarua District. The study used a Logit model. Both quantitative and qualitative data were acquired from primary and secondary sources. Primary data was collected using questionnaires through a survey design. A sample of 164 smallholder farmers was selected using stratified, multi-stage random sampling techniques. Data was analyzed using descriptive statistics and maximum likelihood method using Statistical Package for Social Sciences (SPSS). The study established that socio-economic constraints such as age, gender, household size, farm income, collateral and awareness are critical determinants of access to credit. The study also established that institutional requirements such as costs involved in operating / maintaining bank accounts, loan requirements and transaction costs involved in the credit process influenced access to credit. The study concludes that household socio-economic characteristics and institutional requirements influence access to credit. Key recommendations made include the need by government to deal with bureaucracies involved in land registration to benefit majority of smallholder farmers who remain insecure in the land they use without proof of ownership and also to make easier the registration of lease certificates for those who do not own land and use land on leasehold tenure system. Financial institutions should also put in place less stringent credit requirements and reduce credit costs especially interest rates to make credit more affordable. Keywords: socio-economic and institutional constraints, credit access, smallholder farmers, logit model

Analysis of Farmers’ Perceptions of the Effects of Climate Change in Kenya: the Case of Kyuso District

A cross-sectional analysis was carried out to evaluate how farmers in Kyuso District have perceived climate change. Data was collected from 246 farmers from six locations sampled out through a multistage and simple random sampling procedure. The logistic regression analysis was carried out to assess factors influencing farmers’ perceptions of climate change. The analysis revealed that 94% of farmers in Kyuso District had a perception that climate was changing.  In this regard, age of the household head, gender, education, farming experience, household size, distance to the nearest input/output market, access to irrigation water, local agro-ecology, access to information on climate change, access to extension services, off farm income and change in temperature and precipitation were found to have significant influence on the probability of farmers to perceive climate change. Since the level of perception to climate change revealed by the study was found to be high (94%), the study suggests that more policy efforts should thus be geared towards helping farmers to adapt to climate change. Key words: Climate change, Perceptions, Logistic regression, Kyuso District

Assessment of Farmers’ Adaptation to the Effects of Climate Change in Kenya: the Case of Kyuso District

The study was carried out to assess how farmers in Kyuso District have adapted to the effects of climate change. Survey data was collected from 246 farmers from six locations that were sampled out through a multistage and simple random sampling procedure. The probit regression model was fitted into the data in order to assess factors influencing farmers’ adaptation to the effects of climate change. The analysis revealed that 85% of the farmers had adapted in various ways to the effects of climate change. In this regard, the age of the farmer, gender, education, farming experience, farm income, access to climate information, household size, local agro-ecology, distance to input/output market, access to credit, access to water for irrigation, precipitation and temperature were found to have significant influence on the probability of farmers to adapt to climate change. The study suggests that more policy efforts should thus be geared towards helping all the farmers in the district to adapt to climate change. Key words: climate change, adaptation, probit regression model, Kyuso District

Analysis of Farmers’ Perceptions of the Effects of Climate Change in Kenya: The Case of Kyuso District

A cross-sectional analysis was carried out to evaluate how farmers in Kyuso District have perceived climate change. Data was collected from 246 farmers from six locations sampled out through a multistage and simple random sampling procedure. The logistic regression analysis was carried out to assess factors influencing farmers’ perceptions of climate change. The analysis revealed that 94% of farmers in Kyuso District had a perception that climate was changing. In this regard, age of the household head, gender, education, farming experience, household size, distance to the nearest input/output market, access to irrigation water, local agro-ecology, access to information on climate change, access to extension services, off farm income and change in temperature and precipitation were found to have significant influence on the probability of farmers to perceive climate change. Since the level of perception to climate change revealed by the study was found to be high (94%), the study suggests that more policy efforts should thus be geared towards helping farmers to adapt to climate change

Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning

Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences.</p