Azzarri, Carlo

Zhe Guo

CGSpace

    Site selection for the Africa RISING project in Northern Ghana      Produced by:  Zhe Guo and Carlo Azzarri, International Food Policy Research Institute (IFPRI)  Published by:  International Food Policy Research Institute (IFPRI)  January 2013  www.africa-rising.net         The Africa Research In Sustainable Intensification for the Next Generation (Africa RISING) program comprises three research-for-development projects supported by the United States Agency for International Development as part of the U.S. government’s Feed the Future initiative.  Through action research and development partnerships, Africa RISING will create opportunities for smallholder farm households to move out of hunger and poverty through sustainably intensified farming systems that improve food, nutrition, and income security, particularly for women and children, and conserve or enhance the natural resource base. The three projects are led by the International Institute of Tropical Agriculture (in West Africa and East and Southern Africa) and the International Livestock Research Institute (in the Ethiopian Highlands). The International Food Policy Research Institute leads an associated project on monitoring, evaluation and impact assessment.   This document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License      This document was made possible with support from the American people delivered through the United States Agency for International Development (USAID) as part of the US Government’s Feed the Future Initiative. The contents are the responsibility of the producing organization and do not necessarily reflect the opinion of USAID or the U.S. Government.    Contents Introduction ............................................................................................................................................................................. 1 Review of biophysical and socio-economic characteristics in the proposed northern districts ................................................. 4 Proposed stratification for site selection ................................................................................................................................ 13 Conclusions ............................................................................................................................................................................ 17    1  Introduction The Africa RISING program of the USAID-Feed the Future initiative proposes to initiate and test interventions to enable Sustainable Intensification (SI) of agriculture in three major regions of Africa by working in three “mega-sites” which exemplify the main climatic and human characteristics of these regions.  Some districts in Northern Ghana and Southern Mali are the target areas in the West Africa mega-site. This report provides the site selection analysis for the districts and communities in Northern Ghana, while the site selection in Southern Mali is managed by the ICRISAT-Mali office. Nadowli, Wa, and Wa west from upper west region; Bawku West, Kassena Nankana, Bongo, and Talensi Nabdam from upper east region; Savelugu Nanton, Tolon-Kumbungu, Yendi, and Tamale from the northern region have been selected as focused areas in the country. The districts are highlighted in Figure 1. Collaborators on the ground chose communities within each district, also illustrated in the map. The full list of the 52 proposed sites/communities is shown in Table 1 with district names, community names and their associated location.    Figure 1. Focused districts and proposed sites in Northern Ghana     2  districts community longitude Latitude tolong nyorin -1.03546 9.49404 tolong balinkpen -0.99184 9.45408 tolong cheyohi -0.98544 9.43944 tolong gizaa -1.04614 9.46353 tolong tingoli -1.01167 9.37538 kassena-nankana tampola -1.08969 10.77788 kassena-nankana bonia -1.12764 10.87064 kassena-nankana gia -1.13678 10.91069 mion zuro -0.55516 9.41941 mion zakpalsi -0.3194 9.46281 mion sanzei -0.26779 9.38305 mion tuya -0.45429 9.40533 savelugu zosali -0.83315 9.8968 savelugu nakpanzoo -0.81673 9.75487 savelugu kpallung -0.78154 9.6845 savelugu tibali -0.84488 9.66808 savelugu botingli -0.78975 9.6106 wa west janke -2.59458 10.06642 wa west tomare -2.72591 10.09518 wa west guabe -2.71903 10.03452 wa west oir -2.61272 10.01138 wa west nakori -2.54581 10.01576 nadowli kulankagla -2.7432 10.27752 nadowli natordari -2.62636 10.24522 nadowli papu -2.58076 10.23668 nadowli goli -2.63016 10.29462 nadowli bili -2.58171 10.32502 bawku west azoawera -0.49997 10.79173 bongo sabulunga -0.82034 10.93778 talensi-nabdam pelungu -0.68861 10.79233 bawku west buliga -0.42078 10.71754 kassena nankana naaga -1.00759 10.59122 kassena nankana doba -1.04008 10.86181 kassena nankana nyangua -1.05948 10.93018 kumbungu kpirim -0.98988 9.57207 kumbungu logushegu -0.96302 9.5421 kumbungu gbanzogu -0.95095 9.49927 tamale mun yimahinayili -0.672 9.38491 tamale mun jerigo -0.72985 9.32981 yendi kulukpene -0.0698 9.42348 yendi nasiuk 0.00457 9.6119 savelugu dinga -0.96566 9.88546 savelugu tarikpaa -0.90299 9.63615 savelugu zugu -0.93467 9.57761 3  nadowli paria -2.69981 10.41093 nadowli tachiripie -2.63232 10.40198 nadowli bakpa -2.52144 10.44606 nadowli wola -2.52075 10.38958 wa municipal sako -2.45463 10.1568 wa municipal kodali -2.3954 10.11479 wa municipal dodayiri -2.38921 10.03146 wa municipal sakaripea -2.34306 10.01287  Table 1. Proposed sites in Northern Ghana  4  Review of biophysical and socio-economic characteristics in the proposed northern districts In order to stratify and characterize the focused districts, a review of available spatial biophysical and socio-economic data layers is presented. The main purposes are to: 1. Understand the spatial pattern and homogeneity of each of the candidate data layers; 2. Choose the appropriate dataset for the stratification analysis.  Among the candidate layers on population density, Agro-Ecological Zones, precipitation, elevation, slope, farming system, market access, Length of Growth Period (LGP), and land cover datasets (listed in Table 2), only some of them were deemed to be appropriate to characterize and stratify districts in Northern Ghana, given their spatial variability.  Table 2. Characteristics of the candidate data layers The relevant variables are first mapped in order to visualize their spatial distribution, and then they are aggregated by classes.    Datasets Spatial Resolution Year SourcePopulation density 1 sqkm 2000 CIESINAgro-Ecological Zones ~10 sqkm - IIASA50 sqkm long term (> 50 years)average CRU1 sqkm long term (> 50 years)average WorldClim100 sqkm long term (> 50 years)average NASA POWER50 sqkm long term (> 50 years)average GPCCElevation 1 sqkm - USGSSlope 1 sqkm - USGSFarming systems shape file - John Dixon (2012 version)Market access 1 sqkm 2000 HarvestChoice5km ICRISAT~10 sqkm long term (> 50 years)average IIASALand cover 1 sqkm 2000 GLC2000PrecipitationLength of Growth Period5  1. Population density Population density in Northern Ghana is generally low. Most of the area shows population density lower than 100 persons per squared kilometer, and it is classified into 3 categories with the following cut-offs: less than 15, 16 -50, and greater than 50.  Figure 2. Population density   6   2. Elevation There are many datasets available on elevation for Ghana: the USGS Hydro1k data layer has been chosen because most of the other data used in Africa RISING site selection analysis is at 1km resolution. In order to avoid arbitrary selection of cut-off values, the quintile of elevation distribution at 1km pixel level has been used. In Northern Ghana, the spatial heterogeneity in the measurement of altitude is relatively low, as the elevation ranges from 87 meters to 393 meters.  Figure 3. Elevation   7   3. Precipitation Even though there are several publicly available precipitation data layers, most of them are more suitable to global studies than to country or sub-national analysis, being at a very coarse resolution. There are two methods to derive precipitation data point at the pixel level. One is from weather station records with spatial interpolation. The second method is from satellite observation.  The data from WorldClim has the highest spatial resolution, at 1km (Error! Reference source not found.4). Nevertheless, these data need to be used with caution, as their reliability has been questioned from various parts.  Other possible climatic data sources are CRU, NASA POWER, and GPCC, being all at half degree resolution. Their main drawback is the very coarse resolution, which makes them inadequate for the analysis on the focused districts.  To overcome the low resolution problem, the suggested option is to look at the length of growth period, analyzed in the next paragraph.   Figure 4. Long-term average precipitation (source: WorldClim)    8  4. Length of growth period The length of growth period, a good proxy of agriculture potential, measures how many continuous suitable days are available for the crop to grow based on soil water capacity holding, soil moisture, temperature, and elevation. Two sources of length of growth period datasets are proposed. One is by IIASA at global scale at 10km2 resolution. The other one is by ICRISAT and is at a finer resolution level (5km2). Both maps are displayed in Figure 5 and 6. For this analysis, the LGP by ICRISAT is used, as it is at a higher resolution and deemed to be more reliable. Northern Ghana shows a clear gradient in terms of agriculture potential from North to South looking at ICRISAT data, which makes its use particularly appropriate in the stratification analysis.  The continuous distribution of LGP in Figure 6 was classified in four groups, with a flexible first threshold of 162 days (taken as the first quartile), and a predetermined thresholds of 180 and 190 days, shown in Figure 7 below. The four classes are then used for stratification purposes. According to this classification, the focused districts in the Upper West region are classified into low agriculture potential, while the Yenti district has the highest potential.  Figure 5. Length of growth period (source: IIASA)   9   Figure 6. Length of growth period (source: ICRISAT) 10   Figure 7. Classified LGP from ICRISAT (four classes: low, med-low, med-high, high)    11  5. Market Access Market access is largely used as an indicator of accessibility. The tercile classification (high, medium, and low) based on travel time in minutes to the nearest city with at least 50 thousand people is applied in this analysis. The district of Tamale has the highest accessibility, while Wa has the lowest.   Figure 8. Market access    12  6. Land cover There are various land cover datasets available at global scale. One of the commonly used is by GLC 2000. The map is shown in Figure 9.   Figure 9. Land cover    13  Proposed stratification for site selection After a review of the candidate variables, the variables of length of growth period by ICRISAT and market access are the final chosen variables used for the stratification process. The LGP is classified into 4 classes: low, med-low, med-high, high. The market access layer is classified into 3 classes: low, med, high. The cut-offs are shown in Table 3.   Class Length of Grow period Market access Low <= 162 >= 200 Med Low 162 – 180 100 - 200 Med high 180 – 190  High >190 <= 100 Table 3. Proposed variables and their cut-offs  The chosen variables are summarized at the district level, and average values are reported in Table 4.  Admin names Length of growth period Population density Rainfall Elevation Market access  Nadowli  166 1,350 990 283 187  Bawku West  158 85 961 205 123  Kassena Nankana  152 181 953 208 165  Bongo  154 173 917 224 148  Talensi Nabdam  161 142 994 194 148  Wa  178 1,350 1,039 298 201  Wa West  180 19 1,048 268 160  Savelugu Nanton  181 866 1,067 138 176  Tolon-Kumbungu  183 1,797 1,057 133 186  Yendi  194 1,195 1,176 170 181  Tamale  189 1,780 1,105 146 60 Table 4. Average values at the district level Similarly, Table 5 reports average values of the same variables at the community level. 14   Table 5. Average values at the community level  Community District longitude latitude Land cover LGP Population density Rainfall Elevation Market accessnyorin tolong -1.03546 9.49404 19 188  31 1074 137 105balinkpen tolong -0.99184 9.45408 19 187  31 1081 144 153cheyohi tolong -0.98544 9.43944 19 189  31 1087 158 114gizaa tolong -1.04614 9.46353 19 188  31 1080 159 40tingoli tolong -1.01167 9.37538 19 191  50 1093 171 82tampola kassena-nankana -1.08969 10.77788 19 155  93 962 167 62bonia kassena-nankana -1.12764 10.87064 19 152  93 948 173 62gia kassena-nankana -1.13678 10.91069 19 150  93 945 212 171zuro mion -0.55516 9.41941 11 190  9 1117 122 167zakpalsi mion -0.3194 9.46281 11 190  9 1165 196 188sanzei mion -0.26779 9.38305 11 192  9 1183 213 64tuya mion -0.45429 9.40533 10 190  9 1140 154 82zosali savelugu -0.83315 9.8968 11 179  26 1053 129 139nakpanzoo savelugu -0.81673 9.75487 11 182  26 1057 115 60kpallung savelugu -0.78154 9.6845 11 183  26 1073 144 198tibali savelugu -0.84488 9.66808 19 183  26 1068 138 50botingli savelugu -0.78975 9.6106 19 185  886 1077 142 55janke wa west -2.59458 10.06642 11 174  35 1021 282 169tomare wa west -2.72591 10.09518 11 173  35 1017 252 83guabe wa west -2.71903 10.03452 11 174  35 1033 254 121oir wa west -2.61272 10.01138 11 177  35 1035 286 139nakori wa west -2.54581 10.01576 19 177  35 1032 302 37kulankagla nadowli -2.7432 10.27752 11 167  16 969 259 105natordari nadowli -2.62636 10.24522 11 167  16 990 308 211papu nadowli -2.58076 10.23668 11 167  16 991 295 115goli nadowli -2.63016 10.29462 19 166  16 975 287 144bili nadowli -2.58171 10.32502 11 166  16 974 287 179azoawera bawku west -0.49997 10.79173 19 160  86 977 222 121sabulunga bongo -0.82034 10.93778 18 154  162 903 224 49pelungu talensi-nabdam -0.68861 10.79233 19 159  121 981 219 197buliga bawku west -0.42078 10.71754 19 162  86 992 224 157naaga kassena nankana -1.00759 10.59122 19 161  93 994 160 101doba kassena nankana -1.04008 10.86181 18 153  93 924 175 23nyangua kassena nankana -1.05948 10.93018 18 151  93 888 199 161kpirim kumbungu -0.98988 9.57207 19 186  31 1067 135 35logushegu kumbungu -0.96302 9.5421 19 186  31 1074 143 30gbanzogu kumbungu -0.95095 9.49927 19 188  31 1084 167 100yimahinayili tamale mun -0.672 9.38491 11 189  105 1110 119 42jerigo tamale mun -0.72985 9.32981 19 191  105 1113 142 16kulukpene yendi -0.0698 9.42348 11 194  58 1194 171 7nasiuk yendi 0.00457 9.6119 11 188  9 1164 153 30dinga savelugu -0.96566 9.88546 11 178  26 1046 123 322tarikpaa savelugu -0.90299 9.63615 19 185  26 1068 139 195zugu savelugu -0.93467 9.57761 19 185  31 1074 140 51paria nadowli -2.69981 10.41093 19 164  16 943 276 144tachiripie nadowli -2.63232 10.40198 19 164  16 961 306 146bakpa nadowli -2.52144 10.44606 11 162  16 983 339 139wola nadowli -2.52075 10.38958 11 164  16 981 326 219sako wa municipal -2.45463 10.1568 11 172  16 1021 352 45kodali wa municipal -2.3954 10.11479 11 172  35 1021 295 57dodayiri wa municipal -2.38921 10.03146 11 177  35 1038 340 100sakaripea wa municipal -2.34306 10.01287 11 177  35 1039 324 12815  In order to reduce the number of combinations, the final variables used are length of growth period and market access. The latter variable has been chosen as being a proxy of population density (relatively homogeneous in Northern Ghana, therefore not suitable for stratification purposes). LGP is classified into 4 classes (low, med-low, med-high, high), while market access is classified into 3 classes (low, med, high). LGP and market access classes are then matched to stratify the districts in Northern Ghana. Average values and combined classes of LGP and market access at the district level are listed in Table 6 and the associated map is displayed in Figure 10.  Admin name LGP LGP class Market access Market access class Final class Nadowli 166 Med-Low 187 Med Med-Low LGP * Med mkt Bawku West 158 Low 123 Med Low LGP * Med mkt Kassena Nankana 152 Low 165 Med Low LGP * Med mkt Bongo 154 Low 148 Med Low LGP * Med mkt Talensi Nabdam 161 Low 148 Med Low LGP * Med mkt Wa 178 Med-High 201 Low Med-High LGP * Low mkt Wa West 180 Med-High 160 Med Med-High LGP * Med mkt Savelugu Nanton 181 Med-High 176 Med Med-High LGP * Med mkt Tolon-Kumbungu 183 Med-High 186 Med Med-High LGP * Med mkt Yendi 194 High 181 Med High LGP * Med mkt Tamale 189 Med-High 60 High Med-High LGP * High mkt Table 6. LGP, market access, and their intersection at the district level   16  Figure 10. Combination of Length of growth period (4 classes) and Market access (3 classes)   17  Conclusions The stratification process uses length of growth period (LGP) and market access as proxies of agriculture potential and socio-economic integration in the food value chain, respectively. Combining the two criteria, six unique classes are derived. It is suggested to choose the intervention communities in five classes/strata, perhaps avoiding the Tamale district (the only one with mid-high LGP and high market access), because its small size does not allow ruling out contamination of control sites given the inevitable proximity to action sites. Given the close proximity between the 29 intervention and the 23 counterfactual communities/sites previously identified, and the new stratification proposed in this document, a re-selection of communities in the 11 districts is advised. The re-selection would need to guarantee an adequate coverage of the spectrum of biophysical and socio-economic conditions prevailing in the targeted districts, allowing for a broad assessment of the interventions in areas with different agricultural potential.  In particular, it is recommended to keep the identified 52 communities, but disregard the previous distinction between intervention and counterfactual communities. Action sites/communities should be chosen within the red circles, while control communities should be chosen within the white circles, shown in Figure 10. This way the control communities would be located within the same stratum identified, but with enough remoteness to reasonably avoid potential contamination between actions and counterfactual sites, although the final selection between intervention and control areas should be informed by local knowledge (e.g. to check if they share markets or other public facilities and verify their actual agricultural potential). Only after intervention communities will be chosen, control communities will be selected randomly within the identified control areas. 

Site selection for the Africa RISING project in northern Ghana

https://cgspace.cgiar.org/bitstream/10568/42254/1/AR_ghana_site_selection.pdf

Site selection for the Africa RISING project in northern Ghana

Abstract

Similar works

Full text

Available Versions

CGSpace