Can agriculture support climate change adaptation, greenhouse gas mitigation and rural livelihoods? insights from Kenya

Changes in the agriculture sector are essential to mitigate and adapt to climate change, meet growing food demands, and improve the livelihoods of poor smallholder producers. What agricultural strategies are needed to meet these challenges? To what extent are there synergies among these strategies? This paper examines these issues for smallholder producers in Kenya across several agroecological zones. Several practices emerge as triple wins, supporting climate adaptation, greenhouse gas mitigation, and profitability goals. In particular, integrated soil fertility management and improved livestock feeding are shown to provide multiple benefits across all agroecological zones examined. Triple wins of other agricultural practices are limited to specific agroecological zones. Irrigation and soil and water conservation, for example, are essential for adaptation, mitigation, and profitability in arid areas. The results suggest that agricultural investments targeted toward these triple-win strategies will have the greatest payoff in terms of increased resilience of farm and pastoralist households and global climate change mitigation. To reap the benefits of triple-win strategies will require that policymakers, researchers, and practitioners move away from isolated approaches focused on either adaptation or mitigation or rural income generation toward a more holistic assessment of joint strategies as well as their tradeoffs and synergies.     

Matching solute breakthrough with deterministic and stochastic aquifer models

Two different deterministic and two alternative stochastic (i.e., geostatistical) approaches to modeling the distribution of hydraulic conductivity (K) in a nonuniform (sigma2ln(K)) = 0.29) glacial sand aquifer were used to explore the influence of conceptual model selection on simulations of three-dimensional tracer movement. The deterministic K models employed included a homogeneous effective K and a perfectly stratified 14 layer model. Stochastic K models were constructed using sequential Gaussian simulation and sequential i ndicator simulation conditioned to available K values estimated from measured grain size distributions. Standard simulation software packages MODFLOW, MT3DMS, and MODPATH were used to model three-dimensional ground water flow and transport in a field tracer test, where a pulse of bromide was injected through an array of three fully screened wells and extracted through a single fully screened well approximately 8 m away. Agreement between observed and simulated transport behavior was assessed through direct comparison of breakthrough curves (BTCs) and selected breakthrough metrics at the extraction well and at 26 individual multilevel sample ports distributed irregularly between the injection and extraction wells. Results indicate that conceptual models incorporating formation variability are better able to capture observed breakthrough behavior. Root mean square (RMS) error of the deterministic models bracketed the ensemble mean RMS error of stochastic models for simulated concentration vs. time series, but not for individual BTC characteristic metrics. The spatial variability models evaluated here may be better suited to simulating breakthrough behavior measured in wells screened over large intervals than at arbitrarily distributed observation points within a nonuniform aquifer domain.     

Modelling catchment‐scale erosion patterns in the East African Highlands

Prompt location of areas exposed to high erosion is of the utmost importance for soil and water conservation planning. Erosion models can be useful tools to locate sources of sediment and areas of deposition within a catchment, but the reliability of model predictions of spatial patterns of erosion at catchment scale has seldom been validated against observations. This study aimed to evaluate the performance of a simple empirical model (Morgan, Morgan and Finney model, MMF) in predicting spatial patterns of erosion at two small catchments in the East African Highlands: Kwalei (Tanzania) and Gikuuri (Kenya). Erosion maps predicted by the MMF model were compared with erosion maps obtained by direct survey. In Kwalei, erosion features were especially frequent in fields of annual crops. In Gikuuri, slope was the critical erosion factor, with estimated erosion rates >10 kg m^?2 a^?1 on slopes >18 per cent. Predicted erosion rates were mainly transport‐limited and ranged from <0·01 to 13·50 kg m^?2 a^?1 in Kwalei and 9·29 kg m^?2 a^?1 in Gikuuri. The performance of the MMF model in predicting the spatial patterns of erosion was acceptable in Kwalei, but poor in Gikuuri. However, by excluding the elements at the valley bottoms in Gikuuri Catchment, the performance of the model improved dramatically. The spatial pattern of erosion predicted by the MMF model was driven by the accumulation of surface runoff, which did not consider the possibility of re‐infiltration along the slope. As a result, the MMF erosion patterns predicted by the model increased invariably from the ridges to the valley bottoms, hampering the model suitability for locating areas subjected to high and very high erosion. It is concluded that the model predictions could be substantially improved by introducing a more realistic hydrological component for the prediction of surface runoff along the hill‐slope. Copyright © 2005 John Wiley & Sons, Ltd.