Project Title: Ethiopia's Productive Safety Net Program (PSNP) national baseline database (NBD): Georeferenced site, management, topography, climate, soil carbon, soil fertility indicators, yield and low-cost soil mid-infrared (MIR) analysis result This baseline data was prepared on behalf of the World Bank by the authors: Dawit Solomon (Cornell University, USA), Dominic Woolf (Cornell University, USA), Stefan Jirka (Cornell University, USA), Steve DeGloria (Cornell University, USA), Berhanu Belay (Jimma University, Ethiopia), Geberemedihin Ambaw (Jimma University, Ethiopia), Kefelegn Getahun (Jimma University, Ethiopia), Milkiyas Ahmed (Jimma University, Ethiopia), Zia Ahmed (Cornell University, USA, and CIMMYT, Mexico), and Johannes Lehmann (Cornell University, USA) Author contact for questions and comments: Dawit Solomon (ds278@cornell.edu), Dominic Woolf (d.woolf@cornell.edu), Stefan Jirka (sj42@cornell.edu), Johannes Lehmann (cl273@cornell.ed8) Data source: School of Integrative Plant Science; Crop and Soil Sciences Section; Bradfield Hall; Cornell University; Ithaca, NY 14853 USA Data file name: EthiopiasPSNPCSINationalBaselineDatabaseOct52015.xls Please cite this work as: Solomon, D., Woolf, D., Jirka, S., De'Gloria, S., Belay, B., Ambaw, G., Getahun, K., Ahmed, M., Ahmed,Z., and Lehmann, L. (2015). “Ethiopia's Productive Safety Net Program (PSNP) national baseline database (NBD): Georeferenced site, management, topography, climate, soil carbon, soil fertility indicators, yield and low-cost soil mid-infrared (MIR) analysis results”. A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41299 Funding agency: The PSNP is implemented by the Government of Ethiopia with support from the following development partners: Canadian International Development Agency, Irish Aid, European Commission, Royal Netherlands Embassy, Swedish International Development Cooperation Agency, UK Department for International Development, United States Agency for International Development, World Food Program and World Bank. Rights and Responsibilities for ReUse: This data is shared under a CC BY-NC license (https://creativecommons.org/licenses/by-nc/4.0/); the data are free to use with proper attribution and acknowledgment of the original authors (see citation information above), and may not be used for commercial purposes. Users must also indicate that the conclusions and assumptions made from analysis of these data are not necessarily the view of the original authors, Cornell University, or the project funders (see funding agency information above). Short description of baseline data and objectives: Ethiopia's climate smart initiative (CSI) is tasked with preparing the ground for PSNP's sustainable public work programs to access climate finance, which is broadly defined as financial support channeled by national, regional and international entities for climate change mitigation and adaptation projects and programs to spur and enable the transition towards low-carbon, climate-resilient growth and development. More robust and cost effective analysis and information on above and below ground carbon stocks and projections of future emissions reduction over space and time is required at multiple stages of development and implementation of PSNP' participatory integrated watershed management projects to access climate finance. Specifically, it is needed to establish rigorous baselines and prioritize the location of emissions reduction or sequestration activities for the monitoring, reporting and verification (MRV) of such activities is critical for Ethiopia's PSNP to secure climate finance, and for investors to see appreciable reductions in GHG emissions and return on their investment. These systems must be appropriate in scale and to the region in question, and must have the required flexibility for application in varying land use types. However, accurate carbon accounting methodologies and appropriate local and regional measurement and monitoring techniques in almost all PSNP projects are largely lacking. There are questions as to how PSNP projects are going to demonstrate baseline scenario, which referred to as also business as usual (BAU) scenario, permanence of the sequestered carbon in the project scenario, as well as other climate smart agricultural and environmental services and co-benefits such as soil fertility and productivity enhancements as a result of implementation PSNP' participatory watershed SLM projects. There is also an overall need to promote the availability of information on socio-economic aspects of land degradation and climate change, and improve the integration of such information into impact and vulnerability assessments to have a comprehensive understanding of the many social and environmental co-benefits that this social safety net program brings to resource poor smallholder farming household's in chronically food insecure regions of Ethiopia. These challenges mean for example carbon sequestration gains or prevented losses as a result of implementation of PSNP projects are at times difficult to quantify, and to some extent inhibits the country's ability to leverage and benefits from climate change adaptation and mitigation finance and payments for ecosystem services and co-benefits. Here we have provide multidisciplinary georeferenced national baseline database which include information about livelihoods, type and duration of best management practices, vegetation, geographical and climatic data collected from the comprehensive reconnaissance survey of Ethiopia's PSNP CSI watersheds spread across six regional states, as well as the soil carbon and other critical soil biological, physical and chemical characteristics generated by the standard and cost-effective analytical approaches is now organized into a single national baseline. It is expected that this downscaled baseline database provide both PSNP's sustainable public works practitioners, and stakeholders and investors the possibility to: (i) enable conduct rapid and effective watershed-level assessment and reporting of carbon stock changes and other co-benefit as a result of the implementation current PSNP's integrated watershed interventions, (ii) enable to model and predict future soil carbon capture and sequestration potentials, as well as other environmental and agricultural co-benefits, and (iii) permit spatial and temporal geospatial mapping and scaling up opportunities. Date and geographic location of survey and sample collection: The reconnaissance survey and sample collection for soil carbon and fertility impact assessment following the implementation of Ethiopia's PSNP participatory watershed interventions was conducted in three rounds in 30 different watersheds (i.e., 27 PSNP and 3 non-PSNP) distributed across 27 Woredas in the more chronically food insecure parts of Ethiopia's six regional states (i.e., Afar, Amhara, Oromia, SNNPRS, Somali and Tigray) between November 2013 and July, 2014. Date of soil analysis: The soil analysis on samples collected from Ethiopia's PSNP CSI watershed intervention sites was started in November 2013 and completed in June, 2015. Methodology: Standard soil physical property analysis Soil bulk density: - Bulk density of the soil samples (g/cm3, i.e., bulk density (g/cm3) = dry soil weight (g) / soil volume (cm3) was quantified directly from the core samples collected from the field and dried at 105 degrees C in a drying oven, and subsequently weighed in a digital electronic balance. Soil texture: - Soil particle size distribution into sand, silt and clay, which is a key component of any minimum dataset used for assessing soil quality, was analyzed using the rapid soil texture method designed for processing large volumes of soil samples with accuracy comparable to more conventional tests involving standard hydrometer and pipette techniques Kettler et al. (2001). Soil water: - Water may be preset in soils with chemical compounds, on particle surfaces, in micro- and macro-pores. However, there are three general forms of soil water contents more relevant for evaluating landscape level soil quality changes following the implementation of sustainable land management interventions in (agro)ecosystems: (i) water retained in the soil at field capacity (-0.10 bar) which is also the upper limit of plant available water in the soil, (ii) water retained in the soil at the point where plants start to wilt, which is the lower limit of plant available water - also called permanent wilting point (-15 bar), and (iii) plant available water content, which is the amount of water held in the soil between the field capacity at -0.10 bar and permanent wilting point at -15 bar of that particular soil (Osman, 2013). These values (i.e., soil water held at field capacity, soil water retained at permanent wilting point and plant available water) were measured in the present investigation using the pressure plate extractor method according to Dane and Hopmans (2002). Methodology: Standard soil carbon and soil fertility analysis After visible remnants of roots and other large plant residues were removed, the surface and subsurface soil samples collected from the various PSNP CSI watersheds were air-dried, thoroughly mixed and sieved to pass 2 mm sieve (exceptions are the samples collected for bulk density measurement), prior to standard and low-cost soil chemical and physical analysis. Soil carbon and nitrogen: - Total carbon (TOC) and nitrogen (TN) concentrations in the soil samples were analyzed by dry combustion according to the specifications of Nelson and Sommers, 1996) using a Temperature Conversion Elemental Analyzer (TC/EA). Soil organic matter: - The soil organic matter content of the soil samples was independently analyzed using a simple but high sample volume ashing procedure for routine determination of soil organic matter as described by Storer et al. (1984). This method is consistent with traditional procedures, but eliminates the use of hexavalent chromium as an environmental pollutant which is commonly used in traditional colorimetric techniques. Plant available nutrients, cation exchange capacity (CEC) and percentage base saturation (PBS): - Plant available nutrients and other elements were extracted using Mehlich III extractant (Mehlich 1984), and quantified by Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES). Potential cation exchange capacity (CEC) was calculated by summing the amount of charge per unit soil from all cations extracted by Mehlich III. Wang et al. (2004) found a good correlation between cations extracted using the Mehlich III solution and ammonium acetate at pH 7 and demonstrated that this approach is a cost-effective and scientifically valued approach to get a measure of potential CEC in soil. Percentage base saturation (PBS) was obtained by dividing the total amount of charge per unit soil of Ca, K, Mg and Na by the potential CEC. Soil pH: - Soil pH was determined in deionized water at a soil to solution ratio of 1:2 (w/v) using a combination electronic pH meter according to Hendershot et al. (1993). Suspensions were shaken for 30 min, and then allowed to settle for 1 h before the pH measurement was recorded. Buffer pH was measured from each soil sample in similar manner using modified Mehlich III solution as an extractant at 1:2 soil to solution ratio (w/v). Methodology: Cost-effective mid infrared (MIR) spectroscopy-based soil analysis Soil mid infrared (MIR) spectroscopy: - The mid-infrared spectra from the soil samples were acquired from 4000-602 cm-1 at a resolution of 4 cm-1 using High Throughput Screening eXTension (HTS-XT) Bruker Tensor Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy following procedures outlined in Terhoven-Urselmans et al. (2010) at the International Centre for Research in Agroforestry (ICRAF) in Nairobi, Kenya. Soil samples were finely ground using an agate mortar and pestle and loaded into aluminum micro titer plates. Ground soil samples (to 0.05 micrometer) were filled into four replicate wells, each well was scanned 32 times and four spectra were averaged to account for within sample variability and differences in particle size and packing density. Reference readings were conducted with no sample loaded onto the ATR crystal. The soil carbon and nutrient contents, and other soil physical and chemical characteristics were determined for all samples using predictions via mid-infrared Random Forest (RF) regression model-based analysis calibrated to the data collected directly from analysis of soil carbon using dry combustion and other standard soil wet-chemical and physical measurements. The MIR RF predictions were generated from direct analyses of 436 samples (69%) of the total 628 samples. The application of RF ensemble models in soil science (Grimm et al., 2008; Vagen et al., 2016) is a relatively recent phenomenon, but has the potential to be a powerful approach for the prediction of soil functional properties and mapping of land degradation prevalence in Ethiopia (Vagen et al.,2013). Abbreviations and column headers (Tab Separated): Item Detailed information Code Column Sample identifiers Sample number SANO 1 Sample identifiers Laboratory sample code LACO 2 Sample identifiers Bulk density sample code BDCO 3 Sample identifiers Unique georeferenced site identifier UQSIORG 4 Sample identifiers CSI livelihood zone CSLZ 5 Site description CSI Region REGION 6 Site description CSI Zone ZONE 7 Site description CSI Woreda WOREDA 8 Site description CSI Kebele KEBLE 9 Site description CSI watershed WATSHED 10 Site description CSI watershed sub-site WATSHEDIS 11 Site description Carbon Benefits Project code CBPC 12 Sustainable land management Sustainable land management PSNPSCEN 13 Sustainable land management Land degradation and development issue PSNPDMAN 14 Sustainable land management Land use PSNPLU 15 Sustainable land management Land use type and SLM objectives PSNPOBJ 16 Sustainable land management Detailed physical and biological SLM PSNPPYBI 17 Sustainable land management Land cover LANDC 18 Sustainable land management SLM intervention PSNPMANT 19 Sustainable land management SLM intervention land use PSNPLUS 20 Sustainable land management SLM intervention land use land cover PSNPDLUS 21 Sustainable land management Year SLM started PSNPYR 22 Sustainable land management Time PSNPNOYR 23 Sustainable land management SLM intervention land use land cover and years PSNPMANYR 24 Sustainable land management Physical measures PHYMES 25 Sustainable land management Biological measures BIOMES 26 Sustainable land management "Major trees, shrubs and bushes" TRSP 27 Sustainable land management Major grass spp. GRSP 28 Sustainable land management Livelihood main crops LZMC 29 Sustainable land management Major crops in the field MCIF 30 Sustainable land management Vegetation VEG 31 Sustainable land management Livelihood zone main livestock LZML 32 Sample type Sample profile or surface SATY 33 Sample type Sample name and depth SANA 34 Sample type Sample name and depth bulk density SNBD 35 Sample type Sample depth range SAD 36 Sample type Sample depth actual ASAD 37 Sample type Sample for wet chemical analysis (WCA) Cornell WETC 38 Sample type Sample for mid-infra red (MIR) AFSIS SMIR 39 Sample type Soil fertility trial (SFT) Jimma SSFR 40 Geographical information UTM Watershed centroid latitude UTME 41 Geographical information UTM Watershed centroid longitude UTMN 42 Geographical information Decimal degree watershed centroid Lat LATDD 43 Geographical information Decimal degree watershed centroid Lon LONDD 44 Geographical information Degree minute second watershed centroid Lat LATNDMS 45 Geographical information Degree watershed centroid Lon LONDMS 46 Geographical information African Albers equal area conic projected location Lat X_m 47 Geographical information African Albers equal area conic projected location Lon Y_m 48 Geographical information Watershed intervention site elevation Google Earth ELVGOGE 49 Geographical information Watershed intervention site centroid elevation tablet GPS GPESLVTAB 50 Geographical information Topographic index 2500 TPI2500 51 Geographical information Topographic index 2000 TPI2000 52 Geographical information Topographic index 1500 TPI1500 53 Geographical information Topographic index 1000 TPI1000 54 Geographical information Topographic index 500 TPI500 55 Geographical information Topographic index 250 TPI250 56 Geographical information Topographic index 100 TPI100 57 Geographical information Aspect Aspect 58 Geographical information Elevation DEM_90 59 Geographical information Plan curvature Plan_curv 60 Geographical information Pro_curv Pro_curv 61 Geographical information Slope Slope_deg 62 Geographical information Slope length Slope_len 63 Geographical information Curvature Total_curv 64 Climatic information Mean annual temperature MAT 65 Climatic information Mean monthly precipitation MMP 66 Climatic information Mean annual precipitation MAP 67 Climatic information Monthly potential evapotranspiration MPET 68 Climatic information Potential evapotranspiration PET 69 Climatic information Potential evapotranspiration ratio (PET/MAP) PETR 70 Climatic information Water vapor pressure WVP 71 Climatic information Wind speed WISD 72 Climatic information Mean day length MDYL 73 Climatic information Precipitation deficit PRDF 74 Climatic information Runoff ROFF 75 Climatic information Runoff ratio RURA 76 Climatic information Aridity AR 77 Climatic information Aridity index AIX 78 Climatic information NPP MNPP 79 Climatic information Climatic dependent potential NPP NPPC 80 Climatic information NPP precipitation NPPP 81 Climatic information NPP temperature NPPT 82 Climatic information NPP limited by NPPL 83 Climatic information NPP precipitation sensitivity NPTS 84 Climatic information NPP temperature sensitivity NPPS 85 Climatic information Holdridge zone AECH 86 Climatic information Koeppen climate class KKCL 87 Climatic information Budyko climate class BCL 88 Climatic information Moist major growing season PRC/PET > 0.5 rain fall dependent MGROWSEAS 89 Soil biological properties Root activity ROOTACTV 90 Soil physical properties Soil color COLOR 91 Soil physical properties Bulk density BD 92 Soil physical properties Sand content SAND 93 Soil physical properties Clay content CLAY 94 Soil physical properties Silt content SILT 95 Soil physical properties Texture class TEXC 96 Soil physical properties Drainage DRAN 97 Soil physical properties Available water capacity AWC 98 Soil physical properties Moisture retention at 0.1 bar MC0.1 99 Soil physical properties Moisture retention at 15 bar MC15 100 Soil physical properties Moisture MOIS 101 Soil chemical properties pH-H2O pHWa 102 Soil chemical properties Modified Mellich Buffered pH pHBF 103 Soil chemical properties Desired pH (set at 6.5 best pH for nutrients) DpH 104 Soil chemical properties pH offset pHDIFFR 105 Soil chemical properties Lime required (if pH < desired pH and pHBF >0) LRABRV 106 Soil chemical properties Loss on ignition LOI 107 Soil chemical properties Organic matter OM 108 Soil chemical properties Total C percent TCP 109 Soil chemical properties Total C percent in horizon TCPH 110 Soil chemical properties Total C percent in surface soil TCPS 111 Soil chemical properties Total C concentration TCCO 112 Soil chemical properties Total C concentration in profile TCCOP 113 Soil chemical properties Total C concentration in surface soil TCCOS 114 Soil chemical properties Total C stock TCS 115 Soil chemical properties Total C stock in horizon TCSH 116 Soil chemical properties Total C stock in profile TCSP 117 Soil chemical properties Total C stock in surface soil TCSS 118 Soil chemical properties Total C stock in CO2 equivalent TCSCO2E 119 Soil chemical properties Total C stock in CO2 equivalent in horizon TCSCO2EH 120 Soil chemical properties Total C stock in CO2 equivalent in profile TCSCO2EP 121 Soil chemical properties Total C stock in CO2 equivalent in surface soil TCSCO2ES 122 Soil chemical properties Total N percent TNP 123 Soil chemical properties Total N concentration TNCO 124 Soil chemical properties C/N ratio CNR 125 Soil chemical properties Mehlich III extractable Aluminum Al 126 Soil chemical properties Mehlich III extractable Boron B 127 Soil chemical properties Mehlich III extractable Calcium Ca 128 Soil chemical properties Mehlich III extractable Cobalt Co 129 Soil chemical properties Mehlich III extractable Copper Cu 130 Soil chemical properties Mehlich III extractable Iron Fe 131 Soil chemical properties Mehlich III extractable Potassium K 132 Soil chemical properties Mehlich III extractable Magnesium Mg 133 Soil chemical properties Mehlich III extractable Manganese Mn 134 Soil chemical properties Mehlich III extractable Sodium Na 135 Soil chemical properties Mehlich III extractable Nickel Ni 136 Soil chemical properties Mehlich III extractable Phosphorus P 137 Soil chemical properties Mehlich III extractable Sulfur S 138 Soil chemical properties Mehlich III extractable Zinc Zn 139 Soil chemical properties Mehlich III exchangeable Ca EXCa 140 Soil chemical properties Mehlich III exchangeable K EXK 141 Soil chemical properties Mehlich III exchangeable Mg EXMg 142 Soil chemical properties Mehlich III exchangeable Na EXNa 143 Soil chemical properties CEC CEC 144 Soil chemical properties Percent base saturation PBS 145 Crop yield Crop biomass yield YIELD 146 Low cost MIR prediction - Soil physical properties Bulk density MIR BD_MIR 147 Low cost MIR prediction - Soil physical properties Sand content MIR SAND_MIR 148 Low cost MIR prediction - Soil physical properties Clay content MIR CLAY_MIR 149 Low cost MIR prediction - Soil physical properties Silt content MIR SILT_MIR 150 Low cost MIR prediction - Soil physical properties Available water capacity MIR AWC_MIR 151 Low cost MIR prediction - Soil physical properties Moisture retention at 0.1 bar MIR MC0.1_MIR 152 Low cost MIR prediction - Soil physical properties Moisture retention at 15 bar MIR MC15_MIR 153 Low cost MIR prediction - Soil chemical properties pH-H2O MIR pHWa_MIR 154 Low cost MIR prediction - Soil chemical properties Moisture MIR MOIS_MIR 155 Low cost MIR prediction - Soil chemical properties Loss on ignition MIR LOI_MIR 156 Low cost MIR prediction - Soil chemical properties Organic matter MIR OM_MIR 157 Low cost MIR prediction - Soil chemical properties Total C MIR TCP_MIR 158 Low cost MIR prediction - Soil chemical properties Total C concentration MIR TCCO_MIR 159 Low cost MIR prediction - Soil chemical properties Total N MIR TNP_MIR 160 Low cost MIR prediction - Soil chemical properties Total N concentration MIR TNCO_MIR 161 Low cost MIR prediction - Soil chemical properties C/N ratio MIR CNR_MIR 162 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Aluminum MIR Al_MIR 163 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Boron MIR B_MIR 164 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Calcium MIR Ca_MIR 165 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Cobalt MIR Co_MIR 166 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Copper MIR Cu_MIR 167 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Iron MIR Fe_MIR 168 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Potassium MIR K_MIR 169 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Magnesium MIR Mg_MIR 170 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Manganese MIR Mn_MIR 171 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Sodium MIR Na_MIR 172 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Nickel MIR Ni_MIR 173 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Phosphorus MIR P_MIR 174 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Sulfur MIR S_MIR 175 Low cost MIR prediction - Soil chemical properties Mehlich III extractable Zinc MIR Zn_MIR 176 Low cost MIR prediction - Soil chemical properties Mehlich III exchangeable Ca MIR EXCa_MIR 177 Low cost MIR prediction - Soil chemical properties Mehlich III exchangeable K MIR EXK_MIR 178 Low cost MIR prediction - Soil chemical properties Mehlich III exchangeable Mg MIR EXMg_MIR 179 Low cost MIR prediction - Soil chemical properties Mehlich III exchangeable Na MIR EXNa_MIR 180 Low cost MIR prediction - Soil chemical properties CEC MIR CEC_MIR 181 Low cost MIR prediction - Soil chemical properties Percent base saturation MIR PBS_MIR 182 Further details about carbon benefits and climate finance for PSNP can be found in the following related project documents: Jirka, S., Woolf, D., Solomon, D., & Lehmann, J. (2015). "Climate finance and carbon markets for Ethiopia's Productive Safety Net Programme (PSNP): Executive Summary for Policymakers." A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41302 Jirka, S., Woolf, D., Solomon, D., & Lehmann, J. (2015). "Climate Finance for Ethiopia's Productive Safety Net Programme (PSNP): Comprehensive report on accessing climate finance and carbon markets to promote socially and environmentally sustainable public works social safety net programs." A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41298 Jirka, S., Woolf, D., Solomon, D., & Lehmann, J. (2015). "Guide to Developing Agriculture, Forestry and Other Land-Use (AFOLU) Carbon Market Projects under Ethiopia's Productive Safety Net Programme (PSNP)."" A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41297 Solomon, D., Woolf, D., Jirka, S., De'Gloria, S., Belay, B., Ambaw, G., Getahun, K., Ahmed, M., Ahmed, Z., and Lehmann, L. (2015). "Ethiopia's Productive Safety Net Program (PSNP): Soil carbon and fertility impact assessment"". A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41301 Solomon, D., Woolf, D., Jirka, S., De'Gloria, S., Belay, B., Ambaw, G., Getahun, K., Ahmed, M., Ahmed,Z., and Lehmann, L. (2015). "Ethiopia's Productive Safety Net Program (PSNP) national baseline database (NBD): Georeferenced site, management, topography, climate, soil carbon, soil fertility indicators, yield and low-cost soil mid-infrared (MIR) analysis results". A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41299 Woolf, D., Jirka, S., Milne, E., Easter, M., DeGloria, S., Solomon, D., & Lehmann, J. (2015). "Climate Change Mitigation Potential of Ethiopia's Productive Safety-Net Program (PSNP)". A World Bank Climate Smart Initiative (CSI) Report. Cornell University. https://ecommons.cornell.edu/handle/1813/41296