Simulation Model Approach on Effect of Manure on Greenhouse Gas Fluxes From Soil in Kaptumo, Kenya.

Abstract

Agriculture, especially livestock keeping contributes significantly to changes in atmospheric concentrations of greenhouse gases (GHGs). Research quantifying exchange of GHGs between the biosphere and atmosphere are important in developing climate change mitigation plans. However, with limited research methods support to scientists, many research projects have faced major challenges in full implementation and this forms the basis of the research methods course which intends to bridge such a gap. The experiment described herein was undertaken through Mitigation of Climate Change in Agriculture project that facilitates developing countries to contribute to climate change mitigation in agriculture and move smallholder systems towards climate smart agriculture. Cattle urine and dung patches are GHG sources in pasturelands which impacts to the global GHG budget, but specific information about these emissions are still missing for Kenya GHG inventory. Therefore this study conducted a RCBD experiment over a wet month to monitor GHG fluxes from cattle manure treated soils, and further used Monte Carlo simulation of uncertainty analysis that showed cattle urine impact N20 emission at 68%. The results showed highest N20 emission (85.72 ug m-2 h-1 ) on plot with dung-urine combined treatment. CH4 highest emission was 0.97 (mg m-2 h-1 ) from plot with dung and CO2 highest emission (320.00 mg m-2 h-1 ) from dung-urine plot. Multivariate regression analysis showed that urine, dung-urine and dung treatments were statistically significant in explaining the effect of N20, CH4 and CO2 respectively at (P :s 0.05). This study was successfully accomplished through use of efficient data management and organization plan. Therefore, concluding that all research projects require a data management plan that is well designed by a research methods support, before conducting any research.