Beidou Xi
China’s effort to mitigate soil organic carbon (SOC) loss caused by rapid land use changes over the last two decades faces great challenges. Generally, land use change projects in China have been performed without considering the mechanisms involved in the link between land use change and SOC dynamic. Such situation will likely increase the climatic and environmental risks brought by land use changes. In this paper, we illustrate why most studies over the past several decades in China have been unable to provide significant guiding information for what kind of land use can be adopted to benefit the climate and ecological environments. In addition, we recommend the combination of soil organic matter fractionation with radiocarbon assessment, which researchers are working on to better predict the dynamic trends of SOC under land use change and present several proposals in regard to how to sequester more carbon in soils after land use change.
Keywords: Land use change; Radiocarbons; Fractionation of soil organic matter; Soil carbon sequestration
Introduction
Land use change and SOC dynamics Globally, soils store more than twice the amounts of carbon present in atmospheric CO2 . SOC stock is determined by the balance of net carbon inputs to the soil (e.g. organic matter) and net carbon losses from the soil (e.g. CO2 , dissolved organic carbon, and the loss through erosion). Land use change is identified as the main driving for the balance between carbon inputs and losses in soil [1,2]. Therefore, changes in land use and land management are important causes of SOC store variation; such variation could lead to a marked climate change because altering in climate patterns is associated with atmospheric CO2 concentration [3]. Approximately 545 Gt of carbon have been released in the atmosphere by land use change and the use of fossil fuels, which resulted in an increase in the atmospheric CO2 concentration from the range of 275 ppm to 281 ppm in 1750 to 390 ppm in 2011 and 400 ppm in 2013 [4]. the coping strategies adopted at household level to bridge the gap on food deficit and (v) to assess the nutritional status of children and the elderly in Kisii County. The data used was mainly rainfall and temperature data from meteorological stations and sample data gathered from selected groups. The study population comprised of children between 6 months and 59 months, household heads, elderly people and agricultural officers. Purposive sampling was used to select agricultural officers while multistage sampling was used to select respondents at household level. Primary data was collected by use of a pre-tested questionnaire. The Measurement of Upper Arm Circumference (MUAC) tape was used to collect nutritional status of children while Body Mass Index (BMI) data was obtained from elderly people. Mann Kendall statistic was used to determine whether the trend of rainfall and temperature observed is significant while Chisquare test was used to determine whether the coping strategies observed varied significantly at household level. From the analysis, rainfall has not shown any significant change in Kisii County while temperature trend has been significantly increasing over the years at 95% confidence level. This could explain the observed reduction in river levels. Analysis of crop production and price trends of major food crops in Kisii County showed a decreasing trend of food production leading to increase in price over the years. This meant that farmers could not produce enough to take them to the next harvesting season, making farmers to adopt different coping strategies at household level which differed significantly according to Chi-Square test. Malnutrition status of both elderly people above 59 years and children between 6-59 months were similar with 23% of both children/elderly being severely malnourished. This study has only looked at climatic factors such as rainfall and temperature. Other aspects such as depth of underground water, pH level of soil and the effects of land fragmentation also need to be looked at. This study is important to both farmers in choosing the right crop to plant, and policy makers and planners in formulating the best mitigation and intervention strategies for Kisii County food insecurity problem. This will further contribute to national efforts towards achievement of vision 2030.
Keywords: Climate variability; Food security; Climate change adaptation; Malnutrition
Introduction
The Horn of Africa region has been attracting the attention of many researchers and donors as well due to the food crisis that led to famine conditions and severe food shortage in most parts of Somali, Ethiopia and eastern parts of Kenya during 2011 and 2012 boreal summer [1-6]. While many parts of the world also faced different weather challenges during 2011 and 2012; for instance, July-September Thailand floods in 2011 [7], March-August Texas USA drought [8], high temperatures over western Europe [9], February 2012 European cold spell [10] and the record winter drought of 2011–12 in the Iberian Peninsula [11] among others, the Horn of Africa situation was exacerbated mostly by non-climatic factors such as high global food prices, political instability, chronic poverty, and poor infrastructure among others. Nonetheless, climatic stresses associated with back-to-back failures in both the boreal winter and the boreal spring of 2007, 2008, 2009, 2011 and 2012 played a critical role [3].
Having recognized that climate variability and change is a major driving factor in most economic activities in the region, most governments such as the Kenyan government [12] integrates climate information in her policies. Climate change is quite noticeable, and it is intensifying at an alarming rate in terms of temperature increase and rainfall irregularity in Kenya [13]. For instance, [14] analyzing precipitation and temperature over the Greater Horn of Africa during the 20th and 21st century based on a sample of Coupled Model Intercomparison Project version 3 (CMIP3) models output found that the equatorial eastern Africa region (including the entire Greater Horn of Africa (GHA)) have been experiencing a significant increase in temperature beginning in the early 1980s, in both A1B and A2 scenarios. They further showed that minimum temperatures were projected to increase by more than 2°C above the Long-Term Mean (LTM) by the mid of the 21st century. This was further corroborated by Otieno and Anyah [15] who showed that temperatures were projected to increase at a rate of 0.3/0.40 C/decade under RCP4.5/8.5 scenarios in both equatorial GHA region leading to an approximate temperature increase of 2/2.50 C by the middle of twenty first century. Apart from the weather effect, food insecurity and famine can be caused by a number of factors such as low harvest, post-harvest losses, under nutrition [16,17], increase in food prices and population increase. While global population is set to grow to approximately 9 billion people by 2050 from the present 7 billion, which would require increased volume of food production, it is estimated that by 2080 the agricultural output in developing countries would decrease by 20% due to climate change [16,18]. In Africa; for instance, about 250 million people are likely to face food insecurity due to crop failure, loss of livestock and lack of water as result of climate change by 2020. It is also estimated that by 2040 drought is likely to reduce the area under cultivation of maize, millet or sorghum by 40-80% in sub-Saharan Africa [19,20].In Kenya more than 500,000 farmers of tea have experienced erratic rainfall, greater frost and high temperatures that have threatened their livelihood [21,22], forcing most of them to device ways of coping with the shortages. In Bungoma County; for example, residents have devised many ways of reducing the impact of food insecurity by reducing the number of meals taken per day, working for cash or food, borrowing money to buy food, sell assets and some resorted to borrowing from sellers which makes them poorer [23]. Post-harvest food losses, the measurable quantitative and qualitative food loss in the post-harvest system [24], have also been a major factor contributing to the net harvest. Controlling post-harvest food loss is a key component in ensuring food security. Losses in post-harvest stage can occur due to various factors such as storage, handling, pests and weather conditions. In sub-Saharan Africa post-harvest losses are approximately at 40% of the total harvested cereals [24]. Once crops have been harvested, the unusual rains can dampen the crop yields and result in mould growth, a common phenomenon in developing countries due to reliance on weather (sun) for drying farm produce. If unfavorable weather conditions prevent the crops from drying, the post-harvest losses become high and at times mycotoxin producing moulds such as Aspersillus flavus may produce aflatoxin which can lead to health-related problems if consumed [25]. Nonetheless, in Kenya post-harvest loss is approximated to be at 50% mainly due to weather impact [24].
The aim of this study is to test the capacity of a novel technique for solar pasteurisation in order to assess its suitability for safely processing water for drinking purposes in developing countries. The device is to be used in environments where it is critical to pasteurise the water by reaching sufficient temperatures. Depending on what temperatures are reached and for how long, this would then provide safe drinking water [6]. In addition, conclusive results will allow for the pasteurisation device to be combined with a Passive Solar- Thermal Pumping system, currently being developed at University College London (UCL). This low-cost technology can be used for purifying drinking water, cooking or alternatively as a fuel-saving device, used to heat up water as an alternative to mainstream expensive, unreliable and unsustainable options. the cause of food insecurity in the County. For instance, Kumba and Francis [26] analyzed the influence of agricultural land use on household food security situation in Kisii central. From their study, natural grass or napier grass had a significant influence on household food security while crops and fruits were not [27] looking at the effect of land fragmentation index, quantity of planting fertilizer and the type of seed used found out that all these factors influence technical efficiency in food production [28]. Also, looking at the implication of land use/cover changes on food production and insecurity in Kisii County found out that the reduction of forest land due to rising demand for more agricultural land and settlement had impacted negatively on soil fertility leading to a decline in food production. While these studies have looked at the aspect of land use/cover change and type of agricultural land use, they didn’t consider the aspects of climate and its effects on food insecurity in Kisii County. Characterizing precipitation and temperature and further assessment of climate variability/change is a fundamental step in providing a baseline for understanding the effects of climate variables on food insecurity in Kenya, especially in Kisii County. This will be important for future climate change impact
assessment and for the management of food situation in Kisii County. The County with a population density ranging from 759 -1009 per square kilometer (Figure 1) and being one of the bread basket regions in Kenya is of utmost interest. Hence in this interrogates (i) the trend of precipitation and temperature in Kisii County for the past 30 yrs, (ii) the effect of the changes in temperature and precipitation on food production in Kisii County, (iii) The perception of farmers on climate and weather information (iv) coping strategies adopted at different household level in Kisii County and (v) nutritional status of the elderly above 59 years and children between 6 to 59 months. In the next section the different data types used and their acquisition method and a discussion of the overall methodological approach is outlined. Section 3 provides a discussion of the overall results. A summary of the overall findings and the main conclusions drawn from the study are provided in section 4.
Data and Methodology
Data
Data used in this study were categorized into two; secondary and primary data. Secondary data which consisted of rainfall and temperature data for the past 30 years (1983-2013) was obtained from Kenya meteorological stations in Kisii County and agricultural production details were obtained from sub-county agricultural offices of Marani and Bomachoge chache. Primary data consisting of Body Mass Index (BMI), nutritional status and trend in river levels were collected through pretested questionnaires. The study population consisted of household heads, children between 6-59 months, Adults over 60 years, County and sub-county agricultural officers involved in food production in Kisii County. The children below 60 months and adults over 60 years were chosen since they are more likely to suffer malnutrition [29].
Sample size and sampling technique: A Purposive sampling
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