Life Cycle Inventories of 5 Rwandan Crops under Status-Quo - PowerPoint PPT Presentation

Kirsten Moore: ELLS 2017 Life Cycle Inventories of 5 Rwandan Crops under Status-Quo Production: Assessment of Locally-Sourced versus Ecoinvent Global Data Kirsten Moore MSc Eur-Organic 2015-2017: Universität Hohenheim, ISARA-Lyon 1

Kirsten Moore: ELLS 2017 • Funded by EU‘s Horizon 2020 • Development of protein-rich food based on traditional EU legumes • Make use of amaranth, quinoa, buckwheat, production • Goal: provide alternate to animal based food, EU bakery products – To address challenges of population growth and increasing ag. demands 2 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 • Funded by BMBF • Develop rapid urban planning methodology • Q: How can the land be used sustainably and efficiently to feed a growing population? – requires country specific data on agricultural input (such as fertilizer, water, land) 3 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Larger Context: • Expected that consumption of meat will grow in Sub- Saharan regions – Population growth, urbanization • Understand potential role of plant proteins with this change • To see how urban food flows can interact with typical production 4 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 To assess environmental impact of these projects: • must have information on current agricultural production 5 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Research Tasks: using Rwanda as an example, • Tasked to create inventories of agricultural production- main crops – As a first step to contribute to the research • However, lack of specific information on agricultural practices – Create generic inventories instead • Assess inventories with other generic datasets – LCI, LCA databases 6 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Study Considerations: Why ‚generic‘? • Inventories are based on available literature and not on field research • to understand the available information at present in research • Inventoried crops- produced in greatest volumes in Rwanda 2013-2016 – Human or animal consumption, stored, commodity • Analyze inputs and outputs into a closed system • Cannot analyze the wider production methods (mixed cropping, rotations, etc.) • Cannot represent each topographical area of production- instead, overall average 7 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Methods and Results 1. Determine Rwanda‘s Top Crops accrd. NISR 2. Literature review for agricultural parameters 3. Inventory Creation 1. Literature review results 2. Ecoinvent Version 3.3- global data for comparison 8 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 1. Determine Rwanda‘s Top Crops accrd. NISR Seasonal Agricultural Surveys: 3 • seasons/year – Subdivided strata of Rwanda Majority of cropland on hillsides (82%) • 3.6% of surveyed operators are “large • scale farmers“ – Average farm size 0.25 ha All crops in each food category considered • – Cereals, tubers and roots, banana, legumes and pulses, veg and fruit, other Total kg produced of each crop for each • year within all surveyed strata 9 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 2. Literature Review for Agricultural Parameters 1. LCA databases 2. Exhaustive literature review Agrifootprint, Econet, ecoinvent, World Food Database ‘[ banana] production’ Rwanda Significant lack: Africa, • ‘[banana] production’ ‘Uganda’ , ‘[banana] Sub-Saharan Africa, production’ ‘Kenya’ , then ‘[banana] Rwanda crops production’ ‘East Africa’ , ‘[banana] production’ ‘Sub-Saharan Africa’ . Often only for large- • scale production with ‘[banana] manure Rwanda’ , or ‘[banana] larger system fertilizer Rwanda’ boundaries for the inventories (ex. Soil to fork, rather than soil to harvest) 10 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 % of NISR food % of total surveyed Top 5 NISR Food Category category 2013- NISR agricultural 2016 production 2013-2016 1 Banana* Banana 100% 29.26% 2 Sweet Potato Tubers and Roots 36.79% 15.01% 3 Cassava Tubers and Roots 31.94% 13.03% 4 Maize Cereals 56.37% 5.03% 5 Sorghum Cereals 26.98% 2.41% 11 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 3. Inventory Creation For perspective on agricultural parameters: • Collect inventories on global average production of same 5 crops • ecoinvent – 3/5 https:// www.livescience.com/45005-banana-nutrition- http://news.hamlethub.com/fairfield/places/45574- http://www.tellspecopedia.com/Ingredients/sorghum/ facts.html stamford-museum-s-maize-exhibit-opens-june-24 12 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Agricultural Parameter Categories 13 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Agricultural Parameter Categories Manure Mineral Fertilizer Cultivation Application Use Average yield 3/5 Rwandan crops Applied to all 5 • • • kg/ha/year applied with inorganic urban Kigali farmers • Average seed fertilizers apply levels dependent • requirement kg/ha 14.1% of maize on crop N uptake • Biomass energy of farms (interview Kigali • dried crop yield 0.4% cassava cooperative 2017) • Rooting depth of farms Manure slightly • • crop 2.3% sorghum decomposed with crop • Soil clay content farms residues (Yamano et al. • (NISR 2015, 2016) 2011) • 14 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Agricultural Parameter Categories Pesticide Use Irrigation Harvest Machinery 1/5 crops: maize Not reported for • • Not reportedly used for • 2.8% maize farms any of 5 crops • these 5 crops (NISR 2015,2016) No mechanical • Yet, no published irrigation • literature on amount of High annual • pesticide applied rainfall (1210 Therefore concluded mm/a World • 0/5 crops Bank 2017) 16 Methods and Results Discussion Conclusion Introduction

Kirsten Moore: ELLS 2017 Agricultural Parameter Categories Crop Residue Management Drying and Storage Managed consistentently over 5 crops • Banana, cassava, sweet potato • No burning • eaten fresh or cooked Left on soil after harvest, sown into soil • Maize and sorghum sun dried • with next crop completely (interview Kigali Unavoidable crop residues taken up at • cooperative 2017) harvest: fed to farm animals, recycled back into soil via manure (interview Kigali cooperative 2017) • 17 Methods and Results Discussion Conclusion Introduction

Banana Maize Sorghum Cassava Sweet Potato Parameter Category Parameter Unit/ Kirsten Moore: ELLS 2017 Rwanda Global Rwanda Global Rwanda Global Rwanda Rwanda a Lit Review ecoinvent Lit Review ecoinvent Lit Review ecoinvent Lit Review Lit Review 18,581 2,818 2,541 2,932 14,545 Cultivation Yield kg/ha 36,502 9,315 3,860 (NISR 2013-2016 (NISR 2013-2016 avg (NISR 2013-2016 avg (NISR 2013-2016 avg (NISR 2013-2016 avg avg (annual)) (annual)) (annual)) (annual)) (annual)) 13.59 Mineral N- kg 0 0 0 0 (personal calculations 111.59 78.53 6.4 Fertilizer N/ha (NISR 2015, 2016 ) from Fidele 2015, NISR (Kaizi et al. 2012 ) (Fermont et al. 2009) (One Acre Fund 2016) 2015, 2016 ) 13.26 Mineral kg 0 0 0 0 (personal calculations P2O5- P2O5 3.76 54.49 4.01 (NISR 2015, 2016 ) from Fidele 2015, NISR (Kaizi et al. 2012 ) (Fermont et al. 2009) (One Acre Fund 2016) Mineral Fertilizer /ha 2015, 2016) Fertilizer 2.58 Mineral kg 0 0 0 0 (personal calculations K2O- K2O/ 162.36 66.97 4.01 (Kaizi et al. 2012 ) (NISR 2015, 2016 ) from Fidele 2015, NISR (Fermont et al. 2009) (One Acre Fund 2016 ) Fertilizer ha 2015, 2016 ) Mineral kg 0 0 0 0 0 Ca- CaO/ 0 283.17 24.01 (Assumption on lack (Kaizi et al. 2012 ) (NISR 2015, 2016) (NISR 2015, 2016 ) (One Acre Fund 2016) of data) Fertilizer ha 88.06 57.81 11.69 148.57 95.03 (personal calculations (personal calculations (personal calculations (personal calculations (Personal from Yamano et al. from Yamano et al. from Yamano et al. from Yamano et al. calculations from Manure 2011, KTBL 2015, NISR 2011, KTBL 2015, NISR 2011, KTBL 2015, NISR 2011, KTBL 2015, NISR Manure kg/ha 3,504.24 0 0 NISR 2015 and Use 2014-2016, Hudson 2014-2016, Hudson 2014-2016, Hudson 2014-2016, Hudson 2016 data, KTBL Institute of Mineralogy Institute of Mineralogy Institute of Mineralogy Institute of Mineralogy 2015, Yamano et al. 2017 and Nutrition 2017 and Nutrition 2017 and Nutrition 2017 and Nutrition 2011 ) Value 2017 ) Value 2017 ) Value 2017 ) Value 2017 ) Amount of 0 0 0 Active 0 0 Pesticide kg/ha 4.2 2.33 0.15 (Mukuralinda et al. (NISR 2013-2016 avg (NISR 2013-2016 avg Ingredient (NISR 2015, 2016 ) (One Acre Fund 2016) 2008) (annual)) (annual) ) Pesticide 18 Methods and Results Discussion Conclusion Introduction