This Innovafrica project data on agricultural food value chains.txt file was generated on 2022-06-23 by Giacomo Branca, Luca Cacchiarelli, Donald Njarui, Esther Lupafya, Feyisa Hundessa, Joseph Hella, Mercy Mburu, Mokhele Moeletsi, Mufunanji Magalasi, Mupenzi Mutimura, Chiara Perelli, Alessandro Sorrentino GENERAL INFORMATION 1. Title of Dataset: Innovafrica project data on agricultural food value chains 2. Author Information A. Principal Investigator Contact Information Name: Giacomo Branca Institution: Tuscia University Email: branca@unitus.it B. Associate or Co-investigator Contact Information Name: Luca Cacchiarelli Institution: Tuscia University Email: cacchiarelli@unitus.it Name: Donald Njarui Institution: Kenya Agricultural and Livestock Research Organization Email: donaldnjarui@yahoo.com Name: Esther Lupafya Institution: Soils, Food and Healthy Communities Email:elupafya@gmail.com Name: Feyisa Hundessa Institution: Haramaya University Email: feyohunde@gmail.com Name: Joseph Hella Institution: Sokoine University of Agriculture Email: jhella@sua.ac.tz Name: Mercy Mburu Institution: Kenya National Farmers' Federation Email: mesi88@rocketmail.com Name: Mokhele Moeletsi Institution: Agricultural Research Council of South Africa Email: MoeletsiM@arc.agric.za Name: Mufunanji Magalasi Institution: University of Malawi Email: mufunanjimagalasi@gmail.com Name: Mupenzi Mutimura Institution: Rwanda Agriculture Board Email: mupenzimutimura@gmail.com Name: Alessandro Sorrentino Institution: Tuscia University Email: sorrenti@unitus.it C. Alternate Contact Information Name:Chiara Perelli Institution: Tuscia University Email: chiara.perelli@unitus.it 3. Date of data collection: January - February 2018 4. Geographic location of data collection: Tanzania (Lindi and Rungwe districts), South Africa (Thabomofutsanyana district), Malawi (Mzimba and Dedza districts), Ethiopia (Meta and Kombolcha districts), Kenya (Kirinyaga and Machakos districts) Rwanda (Kirehe and Nyamagabe districts) 5. Information about funding sources that supported the collection of the data: Horizon 2020, Award: 727201 SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: There is no restriction to use these data set. 2. Links to publications that cite or use the data: Branca, G., Cacchiarelli, L., Haug, R., & Sorrentino, A. (2022). Promoting sustainable change of smallholders’ agriculture in Africa: Policy and institutional implications from a socio-economic cross-country comparative analysis. Journal of Cleaner Production, 358, 131949. https://doi.org/10.1016/j.jclepro.2022.131949. Branca, G., Cacchiarelli, L., D’Amico, V., Dakishoni, L., Lupafya, E., Magalasi, M., & Sorrentino, A. (2021). Cereal-Legume Value Chain Analysis: A Case of Smallholder Production in Selected Areas of Malawi. Agriculture, 11(12), 1217. https://doi.org/10.3390/agriculture11121217. Branca, G., & Perelli, C. (2021). ‘Clearing the air’: common drivers of climate-smart smallholder food production in eastern and Southern Africa. Journal of Cleaner Production 270(10), 121900. https://doi.org/10.1016/j.jclepro.2020.121900. 3. Links to other publicly accessible locations of the data: 4. Links/relationships to ancillary data sets: 5. Was data derived from another source? No A. If yes, list source(s): 6. Recommended citation for this dataset: Perelli, Chiara et al. (2022), Innovafrica project data on agricultural food value chains, Dryad, Dataset, https://doi.org/10.5061/dryad.tht76hf26. DATA & FILE OVERVIEW 1. File List: "Data_processed_crop_legume_VC": Dataset related to the crop-legumes value chains and containing variables used in the econometric analyses realised under the WP4 of INNOVAFRICA project. The original data was collected in Tanzania (Lindi district), South Africa (Thabomofutsanyana district), Malawi (Mzimba and Dedza districts), and Ethiopia (Meta and Kombolcha districts). "Data_processed_dairy_VC": Dataset related to the Brachiaria-dairy value chains and containing variables used in the econometric analysis realised under the WP4 of INNOVAFRICA project. The original data was collected in Kenya (Kirinyaga and Machakos districts), Rwanda (Kirehe and Nyamagabe districts) and Tanzania (Rungwe district). 2. Relationship between files, if important: 3. Additional related data collected that was not included in the current data package: 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: i. Why was the file updated? ii. When was the file updated? METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: A dataset was generated by selecting and creating variables of interest based on the baseline survey conducted for the project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - Innovafrica)". The project involved 16 institutions from Europe and Africa and was implemented in six countries namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania. The dataset includes the following information: smallholders' socio-demographic and economic characteristics; improved agriculture practices and seed systems adopted; climate change related aspects; membership in agricultural associations, and access to subsidies, agricultural inputs and credit. 2. Methods for processing the data: Data was processed using Microsoft Excel and STATA Software 3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel 2016; STATA 14 4. Standards and calibration information, if appropriate: 5. Environmental/experimental conditions: Field conditions 6. Describe any quality-assurance procedures performed on the data: Thus study used already curated data from baseline surveys. 7. People involved with sample collection, processing, analysis and/or submission: Giacomo Branca, Luca Cacchiarelli, Donald Njarui, Esther Lupafya, Feyisa Hundessa, Joseph Hella, Mercy Mburu, Mokhele Moeletsi, Mufunanji Magalasi, Mupenzi Mutimura, Chiara Perelli, Alessandro Sorrentino DATA-SPECIFIC INFORMATION FOR:[Data_processed_crop_legume_VC] 1. Number of variables: 32 2. Number of cases/rows: 2221 3. Variable List: [1]crop_ID: ID number of each farm unit [2]country: country where farm unit was located (Tanzania, South Africa, Malawi, Ethiopia) [3]districtcounty: district/county where farm unit was located (Lindi, Freestate, Mzimba, Dedza, Komolcha, Meta) [4]agroecozone: agro-ecological zone where farm unit was located (semi-arid or sub-humid) [5]hoh_gender: gender if the household head (male or female) [6]hoh_age: age of the household head (years) [7]hoh_education: household head level of education (no formal education, adult education, primary, secondary, tertiary, vocational training) [8]hh_total_offfarm_income_USD: household off-farm income (US dollars) [9]hh_total_onfarm_income_USD: household on-farm income (US dollars) [10]hh_total_income_USD: household total income (US dollars) [11]land_area_ha: land size (hectares) [12]improved_seed: quantity of improved seeds used (kg) [13]local_seed: quantity of local seeds used (kg) [14]fertilisers_use: use of fertilisers (yes/no) [16]pesticides_use: use of pesticides (yes/no) [17]other_inputs_use: use of other inputs (yes/no) [18]subsidies_fert: access to subsidies for fertilisers (yes/no) [19]subsidies_seeds: access to subsidies for seeds (yes/no) [20]subsidies_pest: access to subsidies for pesticides (yes/no) [21]credit_access: access to credit (yes/no) [22]climate_change: perception of climate change (yes/no) [23]group_crop: participation to groups (yes/no) [24]EASs_crop: access to Extension Advisory Services (yes/no) [25]food_security_fcs: Food Consumption Score [26]cover_cropping: adoption of cover cropping (yes/no) [27]crop_rotation: adoption of crop rotation (yes/no) [28]intercropping: adoption of intercropping (yes/no) [29]minimum tillage: adoption of minimum tillage (yes/no) [30]mulching: adoption of mulching (yes/no) [31]planting pits: adoption of planting pits (yes/no) [32]tied ridging: adoption of tied ridging (yes/no) 4. Missing data codes: 99 5. Specialised formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: [Data_processed_dairy_VC] 1. Number of variables: 28 2. Number of cases/rows: 1595 3. Variable List: [1]fodder_ID: ID number of each farm unit [2]country: country where farm unit was located (Kenya, Rwanda, Tanzania) [3]districtcounty: district/county where farm unit was located (Kirinyaga, Machakos, Kirehe, Nyamagabe, Rungwe) [4]agroecozone: agro-ecological zone where farm unit was located (arid, humid, semi-arid or sub-humid) [5]hoh_gender: gender if the household head (male or female) [6]hoh_age: age of the household head (years) [7]hoh_education: household head level of education (no formal education, adult education, primary, secondary, tertiary, vocational training) [8]hh_total_offfarm_income_USD: household off-farm income (US dollars) [9]hh_total_onfarm_income_USD: household on-farm income (US dollars) [10]hh_total_income_USD: household total income (US dollars) [11]land_area_ha: land size (hectares) [12]brachiaria_known: farmer knows Brachiaria forage (yes/no) [13]brachiaria_use: farmer uses Brachiaria forage (yes/no) [14]brachiaria_notuse_rsn: reasons underlying the choice not to adopt [15]ls_feedshortage: [14]livestock_local: local animals (number) [15]livestock_exotic: exotic animals (number) [16]livestock_cross: cross animals (number) [17]fertilizers_use: use of pesticides (yes/no) [18]pesticides_use: use of pesticides (yes/no) [19]other_inputs_use: use of other inputs (yes/no) [20]subsidies_seeds: access to subsidies for seeds (yes/no) [21]subsidies_fert: access to subsidies for fertilisers (yes/no) [22]subsidies_pest: access to subsidies for pesticides (yes/no) [23]credit_access: access to credit (yes/no) [24]climate_change: perception of climate change (yes/no) [25]group_fodder: participation to groups (yes/no) [26]EASs_fodder: access to Extension Advisory Services (yes/no) [27]food_security_fcs: Food Consumption Score 4. Missing data codes: n/a 5. Specialized formats or other abbreviations used: