Scale Up of Small Scale Irrigation. Feed the Future Innovation Laboratory for Small Scale Irrigation (ILSSI)

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1 Scale Up of Small Scale Irrigation Feed the Future Innovation Laboratory for Small Scale Irrigation (ILSSI) Need for scale up and its methodological approach The purpose of the scale up is to assess the potential for expanding small-scale irrigation in the project countries. The scale up activity will also study the impacts of large-scale implementation of small-scale irrigation on biophysical factors (e.g. crop yield), environmental sustainability, and economic and family welfare. The scale up activity will utilize crucial inputs to estimate irrigation potential and its biophysical and environmental impacts. Moreover, the socio-economic viability of the technologies and consequent level of adoption will also be investigated. The scale up activity utilizes a hierarchy of biophysical and socio-economic models. Small-scale irrigation interventions will be implemented in areas that are convenient for irrigation practices and agricultural production. The landscape and socio-economic conditions should be studied to identify suitable areas for implementation of small-scale irrigation interventions. Thus, spatial analysis using Multi-Criteria Evaluation in ArcGIS is applied to identify suitable areas for irrigation. Thereafter, the Soil and Water Assessment Tool (SWAT, Arnold et al., 1998), Dynamic Research EvaluAtion for Management (DREAM, HarvestChoice, 1995) and Farm Income and Nutrition (FarmSIM) models will be applied to study the environmental sustainability and socio-economic viability of such interventions. This document presents work in progress and captures the scale up of biophysical and environmental analysis only as an example product.

2 Figure 1. A framework for scaling small-scale irrigation intensification assessment. Scale up from the SWAT model lens SWAT is a comprehensive, process-based, river-basin model (Arnold et al., 1998) which estimates hydrologic and crop production variables. It has been successfully applied in sub-saharan Africa (Dile et al., 2016). The scale up activity will start from the Lake Tana basin where the Robit and Dangesheta watersheds of the ILSSI project are located. The SWAT model was calibrated for the Lake Tana basin using a grid-based approach. The grid-based approach allows one to accurately simulate flow and sediment routing in river reaches (Rathjens and Oppelt, 2012). Developing a grid-based model allows data sharing with the multi criteria irrigation suitability analysis and the DREAM model. Moreover, it also permits calibration of the model using spatial datasets (e.g., evapotranspiration and soil moisture) which are often available at predefined resolutions. For example, the MODIS evapotranspiration dataset is available at 1 km 2 resolution. Evapotranspiration and soil moisture processes are key processes which should be properly represented in the model to accurately estimate the impacts of small-scale irrigation. Therefore, the Lake Tana basin was modeled using 1km-by-1 km watershed discretization. Existing land use map is too coarse for a detailed analysis A land use map for Ethiopia was obtained from the Ministry of Water, Irrigation and Energy (MoWIE); however, this map is too coarse in resolution. For example, agricultural land is divided into two classes: dominantly cultivated and moderately cultivated (Figure 2a). However, studying the potentials and implications of small-scale irrigation requires a more detailed map to identify the location of the dominant crops across the watershed. The International Food Policy Research Institute (IFPRI) has a detailed crop map called the Spatial Production Allocation Model (SPAM), which makes plausible 2

3 estimates of crop distribution within disaggregated units (You et al., 2014). However, the SPAM database provides spatially explicit information only for agricultural land use classes (Figure 2b). Therefore, by extracting the crop distribution from the SPAM database, and maintaining the nonagricultural land use types from the MoWIE map, we produced a land use map for the Lake Tana basin that presents spatially explicit crop maps and other key land use classes (e.g. water, forests and wetland) within the watershed (Figure 2c). Figure 2. Agricultural land use maps: a) Agricultural land use map from the Ethiopian Ministry of Water, Irrigation and Energy; b) dominant crop types in each pixel in the SPAM map; and c) spatially explicit land use map of the Lake Tana basin, showing different crop types and other key land use classes in the watershed 1. Assessing water resources potential and sediment loss The SWAT model can help to assess the water resources potential and soil vulnerability in a watershed (Figure 3). For example, preliminary assessment indicated that the average surface runoff and groundwater recharge in the Lake Tana basin for the period from ranged between 415 mm and 285 mm, respectively. This suggests that there are significant water resources for intensification of small-scale irrigation. The average sediment loss across the watershed was about 40 ton/ha. 1 Note: WHEA = wheat, MAIZ = maize, BARL = barley, SMIL = small millet, SORG = sorghum, OCER/TEFF = other cereals (in SPAM code, which refers to teff in Ethiopia), SESA = sesame seeds, RNGE = range/bush land, WATR = water, WETF = wetland, PAST = pasture land, URBN = urban land. 3

4 Figure 3. Water resources estimate in the Lake Tana basin: a) average annual surface runoff (mm); b) average annual groundwater recharge (mm); and c) average annual sediment yield (ton/ha). The stripes in the surface runoff and groundwater recharge maps are related to issues with the soil data, which were obtained from global sources. Further investigation will be conducted to resolve this issue. Simulating impacts of small-scale irrigation Small-scale irrigation interventions will be implemented in suitable areas for irrigation and simulated with the SWAT model. Thereafter, their impacts on biophysical (e.g., crop yield) and environmental (e.g., upstream and downstream streamflow and sediment flux) variables will be studied. For example, Figure 4 presents the crop yields for rainfed crops, and for dry-season vegetable crops (i.e., onion in this case) using irrigation from shallow groundwater. Most of the land in the Lake Tana basin (75%) is agricultural land, and about 60% of the agricultural land (or approximately 45% of the total watershed area) is suitable for irrigation. More results will be presented on the impacts of the intensification of small-scale irrigation on the upstream and downstream streamflow amounts and variability. 4

5 Figure 4. Average annual crop yields across the Lake Tana basin: a) yields of rainfed crops shown in Figure 2c; and b) yields of dry-season vegetable crops (with onion as a representative crop) cultivated using irrigation from shallow groundwater. Additional work is in progress to refine SWAT modelling efforts and to produce a comprehensive report on the upscaling activities in the Lake Tana basin. Moreover, the biophysical findings will be linked into the economic and market analysis for the scale up activity. References Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., Large area hydrologic modeling and assessment part I: model development. J. Am. Water Resour. Assoc. 34, Dile, Y.T., Karlberg, L., Daggupati, P., Srinivasan, R., Wiberg, D., Rockström, J., Assessing the implications of water harvesting intensification on upstream downstream ecosystem services: A case study in the Lake Tana basin. Sci. Total Environ. 542, doi: /j.scitotenv HarvestChoice, DREAM (Dynamic Research Evaluation for Management 3.1). Rathjens, H., Oppelt, N., SWATgrid: An interface for setting up SWAT in a grid-based discretization scheme. Comput. Geosci. 45, doi: /j.cageo You, L., Wood-Sichra, U., Fritz, S., Guo, Z., See, L., Koo., J., Spatial Production Allocation Model (SPAM) 2005 v2.0. 5