11-13 October 2023 | IRRI, Los Baños, Laguna 2023 WP4 Workshop Report 2 Acknowledgment This workshop was conducted as part of the CGIAR Initiative on Market Intelligence and is supported by contributors to the CGIAR Trust Fund (https://www.cgiar.org/funders). About CGIAR CGIAR is a global research partnership for a food-secure future. CGIAR science is dedicated to transforming food, land and water systems in a climate crisis. Its research is carried out by 13 CGIAR Centers/Alliances in close collaboration with hundreds of partners, including national and regional research institutes, civil society organizations, academia, development organizations and the private sector. www.cgiar.org Front cover photo Researchers using information from the Global Market Intelligence Platform (GloMIP) for pre-breeding discussion. © Neale Paguirigan (IRRI) Report Contributors Lenaerts, Bert Demont, Matty Bayot, Ruvicyn Ynion, Jhoanne Mirzabaev, Alisher https://www.cgiar.org/funders http://www.cgiar.org/ 3 Table of contents 1 About the workshop ............................................................................................................... 4 2 Objec3ves of the workshop ................................................................................................... 6 2.1. Purpose ............................................................................................................................... 6 2.2. Objec3ves ........................................................................................................................... 6 2.3.Contribu3on ......................................................................................................................... 6 3 Program of ac3vi3es ............................................................................................................... 7 4 Session descrip3on ................................................................................................................. 9 4.1. Day 1 Session ...................................................................................................................... 9 4.2. Day 2 Session .................................................................................................................... 10 4.3. Day 3 Session .................................................................................................................... 10 5 Process and outputs ............................................................................................................. 11 5.1. Stocktaking ac3vity ........................................................................................................... 11 5.2. Priori3za3on of traits across TPPs .................................................................................... 11 5.3. Iden3fica3on of data gaps in SPMS, TPP and BP databases ............................................ 12 5.4. Mapping of traits to the 5 CGIAR Impact Areas ............................................................... 12 NUTRITION TEAM .............................................................................................................................. 13 EQUITY TEAM ..................................................................................................................................... 15 PLANET TEAM .................................................................................................................................... 19 5.5. Development of MVMs .................................................................................................... 21 5.6. Refinement of the MVMs (Future ac3vity) ...................................................................... 40 6 List of par3cipants ................................................................................................................ 41 4 1 About the workshop The CGIAR Initiative on Market Intelligence aims at rendering CGIAR and partners’ crop breeding pipelines (BPs) more gender-intentional, impactful, and market-driven by supporting breeding decision making through market intelligence derived from the 5 CGIAR Impact Areas: (1) Nutrition, Health & Food Security; (2) Poverty Reduction, Livelihoods & Jobs; (3) Gender Equality, Youth & Inclusion; (4) Climate Adaptation & Mitigation; and (5) Environmental Health & Biodiversity. There are two types of market intelligence: (1) “bottom-up” or stakeholder elicited market intelligence on stakeholder preferences, needs, priorities and strategies (farmers, processors, traders, consumers, government, seed suppliers, etc.) in market segments; and (2) “top-down” indicators-identified market intelligence on development (impact) challenges and opportunities in market segments. To this end, Work Package 4 (WP4) - Pipeline Investment Cases - has developed the Global Market Intelligence Platform (GloMIP), which consolidates both dimensions of market intelligence in a single platform. This was in collaboration with participating CGIAR Centers and several other Initiatives (i.e., Accelerated Breeding, Breeding Resources, Seed Equal, Foresight and soon Genebanks), as well as with target end-users and stakeholders. GloMIP is a global public good for generating and sharing market intelligence to inform two types of decision making in crop breeding programs: (1) decision on target product profile (TPP) design, trait priorities and alignment of BPs to stakeholder preferences and needs in market segments; and (2) investment prioritization and resource allocation decisions across BPs. In its dual role to align BPs to market intelligence and support investment decisions, GloMIP exchanges information with the Breeding Portal, an internal breeding pipeline management tool developed by the CGIAR Initiative on Accelerated Breeding. These platforms currently exchange databases defining more than 400 seed product market segments (SPMS) and more than 300 TPPs across 24 CGIAR crops. GloMIP enables querying the SPMS through various market intelligence filters, while supporting prioritization decisions through automatic calculation of Impact Opportunities across the 5 Impact Areas at 12 different aggregation levels: (1) UN region, (2) CGIAR region, (3) UN subregion, (4) UN subregion-crop, (5) food system type, (6) CG/non-CG crop, (7) crop group, (8) crop, (9) breeding pipeline, (10) SPMS, (11) country, and (12) country-crop. Impact Opportunities can be used to prioritize investment in market intelligence research across SPMS or across any of these 12 aggregation levels. This can be done before resources are invested in the design of TPPs and the implementation of BPs aligned to these SPMS. To prioritize investment across BPs and make pipeline investment cases to fund future BPs, we need more “complete” metrics, such as projected benefits (PBs) and Return on Investment (RoI). To compute these metrics at the CGIAR portfolio level, we need to adopt a cross-crop trait-by-trait perspective that extracts the relevant data from the available SPMS and TPP databases for all prioritized (essential) traits. First, we need data to project the breadth and depth of the impact of the varietal improvement to be generated by the TPP-guided Breeding Pipelines in the targeted SPMS. It is expected that the SPMS, TPP and other databases in GloMIP and the Breeding Portal are detailed enough to deliver the breadth and depth parameters. Additional trait-related datasets may be needed (e.g., data on biotic/ abiotic stress prevalence for stress tolerance traits). Secondly, additional socioeconomic data may not be needed to https://www.cgiar.org/initiative/market-intelligence/ https://glomip.cgiar.org/ https://www.cgiar.org/initiative/accelerated-breeding https://glomip.cgiar.org/impact-opportunities 5 capture the context of crops in the regions where they are grown. Thirdly, we need data from the Breeding Portal on the investments and breeding efforts 9costs) being channeled to and allocated among the BPs. Finally, we need estimates of the absorption capacity of seed systems and projected speed of adoption of the future varieties to be delivered by the BPs. Photo 1. Global Market Intelligence Platform home page (https://glomip.cgiar.org) 6 The upcoming workshop aims to develop minimum viable models (MVMs) to compute these metrics. These MVMs must leverage the information available in the SPMS (market descriptors), TPPs (breeding blueprint descriptors) and BPs (breeding effort descriptors). These models are called “viable” as they must feature a minimal set of parameters that enable credible priority setting of breeding pipelines at the CGIAR portfolio level across the 5 Impact Areas. MVMs need to be “minimalistic” (at least in the initial phase) to enable all end-users to use and evaluate their validity in a continuous improvement cycle. The MVMs will form the backbone of the Investor Dashboard v1.0 that will be hosted by GloMIP. Once set up, it is expected that these MVMs will be refined over time. The MVMs will form the backbone of the Investor Dashboard, a user-friendly and interactive platform hosted by GloMIP, where investors can support their investment decisions in genetic innovation through visual data on PBs and ROI across crops and regions. 2 Objec8ves of the workshop 2.1. Purpose The workshop aims at developing the technical models behind the Investor Dashboard that enable priority setting of breeding pipelines at the CGIAR portfolio level based on projected benefits (PBs) and returns on investment (RoI). 2.2. Objec3ves 1. To take stock of available data in SPMS, TPP, BP and other databases in GloMIP and Breeding Portal 2. To prioriize traits in TPP database for the development of minimum viable models (MVMs) 3. To idenify data gaps in SPMS, TPP and BP databases for the development of MVMs 4. To map traits in TPP databases to 5 Impact Areas 5. To idenify experise, formaion of 3 Impact Area teams and division of labor within teams 6. To develop MVMs for CGIAR porjolio 7. To develop strategies for refinement and standardizaion of MVMs across crops and countries 8. To design pilot studies to demonstrate and validate the MVMs 9. To iden3fy the next steps and to develop work plan for 2023-2024 2.3.Contribu3on The WP4 workshop will contribute to the following outcomes, outputs, and innovations of the CGIAR Initiative on Market Intelligence, which were clearly prioritized by internal and external stakeholders in response to the declining budget: • End-of-Iniiaive Outcome 2: Research leaders and investors make investment decisions in geneic innovaion using GloMIP and the Investor Dashboard. • Output 4.1: Novel applicaions of methods, metrics, and tools for building pipeline investment cases. • Output 4.2: Pipeline investment cases. • Output 4.3: Global Market Intelligence Plajorm (GloMIP) • Output 4.4: Investor Dashboard (hosted in GloMIP) 7 3 Program of ac8vi8es 11 Oct 2023 08:00-AM Arrival of par6cipants 08:30 AM Welcome message Overview of the workshop Introduc6on of the par6cipants MaCy Demont 09:00 AM Part 1. Stocktaking ac6vity Bert Lenaerts 09:30 AM Part 2. Priori6za6on of traits across TPPs All par6cipants 10:00 AM HEALTH BREAK 10:15 AM Part 2 (cont.). Priori6za6on of traits across TPPs Part 3. Iden6fica6on of data gaps All par6cipants 12:00 PM LUNCH @ IDR 01:30 PM Part 4. Mapping of traits to the 5 CGIAR Impact Areas All par6cipants 03:00 PM HEALTH BREAK 03:15 PM Opening of the hybrid session Synthesis of the morning discussion MaCy Demont 04:30 PM Part 4 (cont.). Mapping of traits to the 5 CGIAR Impact Areas (Impact area priori6za6on and grouping) All par6cipants 05:30 PM End of Day 1 06:00 PM Welcome Dinner at Sulyap Gallery Café All par6cipants 12 Oct 2023 08:00 AM Arrival of par6cipants 08:30 AM Presenta6on of MVM Pilot Studies Bert Lenaerts 09:00 AM Part 5. Development of MVMs for the CGIAR por`olio (group ac6vity) Leads and Rapporteurs: Group 1 Leads: Guy Hareau/ Erick Boy Documenters: Ellanie Cabrera/ Jhoanne Ynion Group 2 Leads: Bert Lenaerts/ Harold Valera Documenters: Neale Paguirigan/ Phoebe Ricarte Group 3 Leads: Alisher Mirzabaev/ Rowell Dikitanan Documenters: Ruvicyn Bayot/ Aileen Maunahan Groups 10:00 AM HEALTH BREAK 8 12 Oct 2023 (cont) 10:15 AM Part 5 (cont.). Development of MVMs for the CGIAR por`olio Groups 12:00 PM LUNCH @ IDR 01:30 PM Part 5 (cont.). Development of MVMs for the CGIAR por`olio (prepara6on of presenta6ons) Groups 03:00 PM HEALTH BREAK 03:15 PM Opening of the hybrid session Synthesis of the morning session MaCy Demont 03:30 PM Presenta6on of the MVMs for the CGIAR por`olio Group leads 05:30 PM End of Day 2 13 Oct 2023 08:00 AM Arrival of par6cipants 08:30 AM Part 6. Refinement of the MVMs for the CGIAR por`olio Groups 10:00 AM HEALTH BREAK 10:15 AM Part 6 (cont.) Refinement of the MVMs for the CGIAR por`olio Groups 12:00 PM LUNCH BREAK 01:30 PM Ac6on planning Groups 02:00 PM Opening of the hybrid session MaCy Demont 02:15 PM Presenta6on of the refined MVMs and work plans (with working break) Group leads 04:00 PM Closing program and synthesis MaCy Demont 05:30 PM End of workshop 9 4 Session descrip8on 4.1. Day 1 Session On the first day, Matty welcomes all participants and introduced the purpose of the workshop highlighting the need for parsimonious models that still credible. WP4 needs models that can build on the existing literature and are flexible enough to take the different databases developed under MI (MSs, TPPs, BPs, Impact Opportunities) as inputs. Photo 2. Matty Demont is welcoming the participants and opening the WP4 workshop Next, Bert gave an overview of existing work being done in WP4 showcasing the data collected and processed. Participants then looked at the database of traits and decided which traits needed to be prioritized based on trait frequency across TPPs and potential impact of each trait. In the afternoon, the trait prioritization exercise continues and expanded into mapping the set of available traits to the 5 CGIAR Impact Areas. This exercise was done by three teams aligned to the five Impact Areas. Online participants joined for the afternoon session. 10 4.2. Day 2 Session Bert opened the second day by giving an overview of ex-ante impact models piloted thus far, covering the Impact Areas poverty, nutrition and climate. Participants then discussed the merits and shortcomings of each method. Each team then broke off to develop a first version of their Minimum Viable Models (MVMs). This exercise continued in the afternoon with the help of online participants. The day ended with each team presenting their draft MVM. 4.3. Day 3 Session On day 3, participants refined their MVMs and mapped available data sources and limitations to come up with a work plan. This exercise continued in the afternoon with the help of online participants. The day ended with each team presenting their final MVM and corresponding work plan. 11 5 Process and outputs 5.1. Stocktaking ac3vity The participants took stock of the seed product market segment (SPMS), target product profile (TPP) and breeding pipeline data in GloMIP and the Breeding Portal. An overview table was prepared by the GloMIP team in advance and may be accessed through this shared folder. 5.2. Priori3za3on of traits across TPPs The participants consolidated traits across TPPs to a manageable set, focusing first on the “essential- improve” traits and ignoring the “essential-threshold” traits. The assumption was that maintaining threshold traits at required minimum or maximum threshold levels has implications for breeding costs and can be omitted from the projected benefit (PB) models in this first generation of Minimum Viable Models (MVMs). For example, rice breeding for slenderness comes at the expense of higher breakage (I.e., lower head rice recovery). If breeders need to breed slender rice without compromising head rice recovery as a threshold trait, they need to invest additional efforts to keep head rice recovery constant, but there is no additional PB. There is no real change if the trait is maintained relative to the baseline, unless the TPP defines head rice recovery as an “essential-improve” trait. An initial list of traits can be found on this folder. Box 1. List of trait groups and value propositions (contributed by Marianne Banziger) 01 Grower – More Yield 02 Grower – Less Loss/ Risk – Climate change 03 Grower – Less Loss/ Risk – Bioic 04 Grower – Less Loss @ locaion: cold, salinity and soil toxicity 05 Grower – Less Loss – Other: mainly lodging 06 Grower – Greater NUE/ Less GHG 07 Grower – Less Labor/ Drudgery 08 Grower – Earlier maturity 09 Grower – Longer storage 10 User – Less Labor 11 User – Processing quanity and quality 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß-carotene 13 Muliplier – Less Cost 14 Other: Plant shape 12 5.3. Iden3fica3on of data gaps in SPMS, TPP and BP databases The participants reviewed the consolidated set of traits to identify any gaps in the SPMS, TPP and BP database. 5.4. Mapping of traits to the 5 CGIAR Impact Areas Box 2. 5 CGIAR Impact Areas Nutriion, health and food security Ending hunger and enabling safe, affordable, healthy diets for the world's most vulnerable people hnps://www.cgiar.org/research/cgiar-porjolio/nutriion-health-food-security/ Poverty reducion, livelihoods and jobs Building on a 50-year track record of li@ing millions out of poverty hnps://www.cgiar.org/research/cgiar-porjolio/poverty-reducion-livelihoods-jobs/ Gender equality, youth and social inclusion Closing the gender gap and enhancing opportuniBes for youth in food, land, and water systems hnps://www.cgiar.org/research/cgiar-porjolio/gender-equality-youth-social-inclusion/ Climate adaptaion and miigaion Improving small-scale producers’ resilience and reducing greenhouse gas emissions from food systems. hnps://www.cgiar.org/research/cgiar-porjolio/climate-adaptaion-miigaion/ Environmental health and biodiversity Increasing producBvity in food systems while staying within environmental boundaries and maintaining biodiversity hnps://www.cgiar.org/research/cgiar-porjolio/environmental-health-biodiversity/ The participants mapped the traits to the 5 Impact Areas by using sticky notes (for writing down traits) and large papers (one large paper per impact area). On each sticky note, participants wrote down the following: (1) the name of the trait; and (2) the impact pathway through which the trait contributes to the Impact Area. Afterwards, the participants had a round table discussion and selected the top 3 impact areas that Work Package 4 will focus on in this phase of the CGIAR Initiatives (2022–2024). The rest will be the focus for the next phase (2025–2027). Team Leads and Co-Leads were identified to further work on the models for the projected benefits of traits in each of the Impact Areas. https://www.cgiar.org/research/cgiar-portfolio/nutrition-health-food-security/ https://www.cgiar.org/research/cgiar-portfolio/poverty-reduction-livelihoods-jobs/ https://www.cgiar.org/research/cgiar-portfolio/gender-equality-youth-social-inclusion/ https://www.cgiar.org/research/cgiar-portfolio/climate-adaptation-mitigation/ https://www.cgiar.org/research/cgiar-portfolio/environmental-health-biodiversity/ 13 NUTRITION TEAM Nutrition, health and food security impact area Contributors: Guy Hareau, Erick Boy, Jhoanne Ynion, Ellanie Cabrera and Matty Demont Photo 3. Nutrition Team 14 Please check the links for the group’s workshop outputs (Impact pathways ppt, xls) Table 1. Direct Impact Pathways (Nutrition, health and food security impact area) Trait Group Impact Area Impact Pathway 01 Grower – More Yield Nutri6on Increase food consump6on >> BeCer nutri6on 09 Grower – Longer Storage Food Security Increase buffer stock >> Food stability >> Increase food supply >> Food security 09 Grower – Longer Storage Health & Nutri6on Increase buffer stock >> Food stability >> Increase food supply >> Food security >> Improve health Example: Brown rice has a lower GI (healthier) but has short shelf life, thus more expensive. If varie6es for brown rice can be improved to have a longer shelf-life it would translate to reduced cost and improved health of the consumers. 12 User – BeCer Nutri6on Health & Nutri6on Increase Mn intake >> Less anemia Increase Zn intake >> Decrease 6me with infec6on >> Increase growth >> Less stun6ng Increase Increase ß-carotene intake >> Reduce diarrhea incidence and vitamin A deficiency Figure 1. Indirect Impact Pathways 15 EQUITY TEAM Poverty reduction, livelihoods and jobs impact area Gender equality, youth and social inclusion impact area Contributors: Bert Lenaerts, Harold Valera, Phoebe Ricarte, Neale Marvin Paguirigan, Valerien Pede, Donald Villanueva, Dehner De Leon and Matty Demont Photo 4. Equity Team 16 Table 2. Impact Pathways (Poverty reduction, livelihoods and jobs impact area) Trait Group Impact Area Impact Pathway Target Beneficiary 01 Grower – More Yield Reduced Poverty Higher income/ Increased revenue for the producer Grower 01 Grower – More Yield Reduced Poverty Lower price Consumer 02 Grower – Less Loss/ Risk – Climate Change Reduced Poverty More stable revenue for people in non-op6mal produc6on areas Grower 03 Grower Less Loss/ Risk - Bio6c 04 Grower – Less Loss @ loca6on: cold, salinity and soil toxicity 05 Grower – Less Loss – Other: mainly lodging 06 Grower – Greater NUE/ Less GHG Reduced Poverty Lower produc6on cost >> Greater profit Grower 07 Grower – Less Labor/ Drudgery Higher Income Less farm work >> More opportunity for other employment Grower 07 Grower – Less Labor/ Drudgery Higher Income Less input cost >> More profit Grower 10 User – Less Labor Higher Income Less household work >> More opportunity for other employment Consumer 08 Grower – Earlier maturity Reduced Poverty Growing more crops in same season/ year >> More output >> Increased produc6on Grower 08 Grower – Earlier maturity Reduced Poverty Crop diversity >> More stable produc6on/ revenue Grower 09 Grower – Longer storage (Postharvest) Reduced Poverty Reduced postharvest losses >> sell at higher prices Grower 11 User – Processing quality and quan6ty Reduced Poverty Price premium Grower 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß- carotene Reduced Poverty Price premium where policy exist Grower 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß- carotene Reduced Poverty More nutri6on >> Less burden of hunger >> Higher labor produc6vity Grower 17 Table 3. Impact Pathways (Gender equality, youth and social inclusion impact area) Trait Group Impact Area Impact Pathway Target Beneficiary 01 Grower – More Yield Higher income If produc/on value is distributed dispropor/onately across genders, then more benefits might go dispropor/onately to women affec/ng the "gender-equity-poverty ra/o" Grower 07 Grower – Less Labor/ Drudgery Non-household employment opportuni2es & improved childcare labor saving for cul/va/on prac/ces which are oDen labor intensive performed by women Grower 07 Grower – Less Labor/ Drudgery Increased youth par6cipa6on labor saving mechanisms (e.g., mechaniza/on, digital agriculture) can aHract youth par/cipa/on Grower 10 User – Less Labor Non-household employment opportuni2es & improved childcare labor saving for household chores which are oDen labor and /me intensive performed by women Consumer 11 User – Processing quality and quan6ty If benefits from price premiums is distributed dispropor/onately across genders (involvement of women in processing), then benefits might go dispropor/onately to women affec/ng the "gender-equity-poverty ra/o" Grower 18 Trait Group Impact Area Impact Pathway Target Beneficiary 11 User – Processing quality and quan6ty Poor processing quality is an important reason for non-adop/on of varie/es when crops are used for household processing/consump/on on farm. Women may have a more influen/al role than men for this aspect given tradi/onal role distribu/ons. Consumer 11 User – Processing quality and quan6ty Improved processing quality may result for some crops in more local processing opportuni/es (for rural markets) = income opportuni/es through value addi/on Consumer 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß- carotene Effect on health of children (born and unborn) Nutri/onal improvements might benefit vulnerable groups differently Consumer 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß- carotene Effect on health of women Nutri/onal improvements might benefit vulnerable groups differently Consumer 12 User – Bener nutriion: mainly Fe, Zn, provitamin A, ß- carotene Effect on health of women and children Less intrahousehold compe//on for nutri/ous food (women and children lesser power Consumer Please check the links for the group’s workshop outputs (Impact pathways ppt, xls) 19 PLANET TEAM Climate adaptation and mitigation Impact area Environmental health and biodiversity Impact area Contributors: Alisher Mirzabaev, Robert Santiago Andrade, John Damien Platten, Rowell Dikitanan, Ruvicyn Bayot, Aileen Maunahan, Alice Laborte and Matty Demont Photo 5. Planet Team 20 Table 4. Impact Pathways (Climate adaptation and mitigation Impact area) Trait Group Impact Area Impact Pathway 01 Grower – More Yield Mi6ga6on More yield – less GHG per unit 01 Grower – More Yield Mi6ga6on More yield-land sparing 02 Grower – Less Loss/ Risk – Climate change 03 Grower – Less Loss/ Risk – Bio6c 04 Grower – Less Loss @ loca6on: cold, salinity and soil toxicity Adapta6on Resilience to bio2c and abio2c stress – more stable food produc2on, prices, access against weather extremes – adapta2on 06 Grower – Greater NUE/ Less GHG Mi6ga6on Greater NUE- less GHG 06 Grower – Greater NUE/ Less GHG Mi6ga6on Longer roots – less GHG 07 Grower – Less Labor/ Drudgery Adapta6on Less labor – more produc2ve labor use 08 Grower – Earlier maturity Adapta6on Early maturity – recover from weather extremes 08 Grower – Earlier maturity Mi6ga6on Early maturity – less submergence 2me under water – less methane 09 Grower – Longer storage 10 User – Less Labor Adapta6on Longer storage – more stock/ more resilience to supply disrup2ons 11 User – Processing quan6ty and quality Mi6ga6on BeHer processing – save 2me and energy 12 User – BeCer nutri6on: mainly Fe, Zn, provitamin A, ß-carotene Adapta6on BeHer nutri2on-beHer health-more adap2ve capacity Table 5. Impact Pathways (Environmental health and biodiversity Impact area) Trait Group Impact Area Impact Pathway 01 Grower – More Yield Biodiversity More yield – land sparing – less deforesta2on – beHer biodiversity 01 Grower – More Yield Environmental Health More yield-higher income-richer farmers-beHer care of environment 02 Grower – Less Loss/ Risk – Climate change 03 Grower – Less Loss/ Risk – Bio6c 04 Grower – Less Loss @ loca6on: cold, salinity and soil toxicity Environmental health & Biodiversity More resilience to bio2c and abio2c stress – less pes2cide and chemical use 06 Grower – Greater NUE/ Less GHG Environmental Health Higher NUE – less fer2lizer, less water, less groundwater extrac2on Please check the links for the group’s workshop outputs (Impact pathways ppt, photo 1, photo 2) 21 5.5. Development of MVMs The participants were grouped into three, corresponding to the prioritized Impact Areas. Each group mapped out the impact pathways and identified the additional parameters that have to be collected from the SPMS, TPP and BP databases for the development of the MVMs. Some MVMs were derived from theory, while others were simple reduced-form or dose-response models based on a single response parameter (e.g., elasticity). The participants also identified data gaps in this process. The team presented the MVMs in plenary. Please check this link for the compilation of the MVMs. 22 Nutrition Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters DALYs Iron, zinc, and vit A direct impact Analyzing the health benefits of biofortified staple crops by means of the disability- adjusted life years approach A handbook focusing on iron, zinc and vitamin A BY ALEXANDER J. STEIN, JONNALAGADDA V. MEENAKSHI, MATIN QAIM, PENELOPE NESTEL, H.P.S. SACHDEV AND ZULFIQAR A. BHUTTA HARVESTPLUS TECHNICAL MONOGRAPHS | 2005 | PAGES: 32https://www.ifpri.org /publication/analyzing- health-benefits- biofortified-staple- crops-means-disability- adjusted-life-years efficacy DALYs bioforification direct impact Keith Lividini, et.al,, Biofortification: A review of ex-ante models, Global Food Security, Volume 17, 2018, Pages 186-195, ISSN 2211-9124, https://doi.org/10.1016 /j.gfs.2017.11.001. (https://www.sciencedi rect.com/science/articl e/pii/S22119124173004 57) ex-ante models DALYs carotenoids aflatoxin direct impact on health: less exposure reduces risk of liver cancer and growth retardation Suwarno, WB et al., 2019 https://doi.org/10.3389 /fpls.2019.00030 Carotenoid concentrations were quantified by Ultra Performance Liquid Chromatography (UPLC) Muzhingi et al., 2017 identify safe levels of aflatoxin (ppb) note: for searching https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 23 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters DALYs carotenoids aflatoxin direct impact on health: less exposure reduces risk of liver cancer and growth retardation Suwarno, WB et al., 2019 https://doi.org/10.3389 /fpls.2019.00030 VICAM’s AflaTest protocol Watertown, MA, United States reference is the source of the parameter DALYs carotenoids aflatoxin direct impact on health: less exposure reduces risk of liver cancer and growth retardation Suwarno, WB et al., 2019 https://doi.org/10.3389 /fpls.2019.00030 Column chromatography from the paper reference is the source of the parameter zinc direct impact on health Lowe NM, et. al., (2022) Biofortified Wheat Increases Dietary Zinc Intake: A Randomised Controlled Efficacy Study of Zincol-2016 in Rural Pakistan. Front. Nutr. 8:809783. doi: 10.3389/fnut.2021.809 783 reference is the source of the parameter iron in beans and pearl millet direct impact on health Finkelstein, J., et.al., Iron biofortification interventions to improve iron status and functional outcomes. Proceedings of the Nutrition Society, 78(2), 197-207. doi:10.1017/S00296651 18002847 standardized cognitive test 8(f)'s memory attention reference is the source of the parameter iron pearl millet & physical activity improvement direct impact on health Scott SP, et.al., (2018). Cognitive Performance in Indian School-Going Adolescents Is Positively Affected by Consumption of Iron- Biofortified Pearl Millet: A 6-Month Randomized Controlled Efficacy Trial. J Nutr. 2018 Sep 1;148(9):1462-1471. doi: 10.1093/jn/nxy113. PMID: 30016516. standardized cognitive test 8(f)'s memory attention reference is the source of the parameter https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 https://doi.org/10.3389/fpls.2019.00030 http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ 24 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Iron in pearl millet direct impact on health Mehta S, et.el., A randomized trial of iron- and zinc- biofortified pearl millet- based complementary feeding in children aged 12 to 18 months living in urban slums. Clin Nutr. 2022 Apr;41(4):937-947. doi: 10.1016/j.clnu.2022.02. 014. Epub 2022 Feb 24. PMID: 35299084. reference is the source of the parameter Iron in pearl millet direct impact on health Finkelstein JL, et. al., (2015). A Randomized Trial of Iron-Biofortified Pearl Millet in School Children in India. J Nutr. 2015 Jul;145(7):1576- 81. doi: 10.3945/jn.114.208009. Epub 2015 May 6. PMID: 25948782. reference is the source of the parameter Iron in potato direct impact on health Burgos G, et. el., (2023) Total Iron Absorbed from Iron-Biofortified Potatoes Is Higher than that from Nonbiofortified Potatoes: A Randomized Trial Using Stable Iron Isotopes in Women from the Peruvian Highlands. J Nutr. 2023 Jun;153(6):1710-1717. doi: 10.1016/j.tjnut.2023.04 .010. Epub 2023 Apr 13. PMID: 37059395; PMCID: PMC10308245. reference is the source of the parameter 25 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Zinc in potato direct impact on health Reyna Liria-Domínguez, et. al., (2023) Biofortified Yellow- Fleshed Potatoes Provide More Absorbable Zinc than a Commonly Consumed Variety: A Randomized Trial Using Stable Isotopes in Women in the Peruvian Highlands,The Journal of Nutrition,Volume 153, Issue 10, 2023,Pages 2893- 2900,ISSN 0022-3166, https://doi.org/10.1016 /j.tjnut.2023.08.028. reference is the source of the parameter Zinc & Non Communicable Diseases direct impact on health Pompano LM, Boy E. Effects of Dose and Duration of Zinc Interventions on Risk Factors for Type 2 Diabetes and Cardiovascular Disease: A Systematic Review and Meta-Analysis. Adv Nutr. 2021 Feb 1;12(1):141-160. doi: 10.1093/advances/nma a087. Erratum in: Adv Nutr. 2021 Jun 1;12(3):1049. PMID: 32722790; PMCID: PMC7850144. reference is the source of the parameter Zinc & Non Communicable Diseases direct impact on health Fabiana F. De Moura ,Mourad Moursi,Abdelrahman Lubowa,Barbara Ha,Erick Boy,Babatunde Oguntona,Rasaki A. Sanusi,Busie Maziya- Dixon Published: June 17, 2015 https://doi.org/10.1371 /journal.pone.0129436 reference is the source of the parameter https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 https://doi.org/10.1371/journal.pone.0129436 26 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters iron in beans for work efficiency direct impact on health Luna SV, et. al., (2020) Increased Iron Status during a Feeding Trial of Iron-Biofortified Beans Increases Physical Work Efficiency in Rwandan Women. J Nutr. 2020 May 1;150(5):1093- 1099. doi: 10.1093/jn/nxaa016. PMID: 32006009; PMCID: PMC7198300. reference is the source of the parameter beans iron Mexico Schoolers direct impact on health Finkelstein JL, (2019) A Randomized Feeding Trial of Iron-Biofortified Beans on School Children in Mexico. Nutrients. 2019 Feb 12;11(2):381. doi: 10.3390/nu11020381. PMID: 30759887; PMCID: PMC6412428. reference is the source of the parameter Wheat Zinc in Bangalore direct impact on health Signorell C, (2023) The Effect of Zinc Biofortified Wheat Produced via Foliar Application on Zinc Status: A Randomized, Controlled Trial in Indian Children. J Nutr. 2023 Oct;153(10):3092- 3100. doi: 10.1016/j.tjnut.2023.08 .013. Epub 2023 Aug 25. PMID: 37633331. reference is the source of the parameter Zinc in Maize direct impact on health Chomba E, et. al., (2015) Zinc absorption from biofortified maize meets the requirements of young rural Zambian children. J Nutr. 2015 Mar;145(3):514-9. doi: 10.3945/jn.114.204933. Epub 2015 Jan 21. PMID: 25733467; PMCID: PMC4770937. reference is the source of the parameter 27 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Zinc in Rice preschoolers direct impact on health Jongstra R, et. al., (2022). The effect of zinc-biofortified rice on zinc status of Bangladeshi preschool children: a randomized, double-masked, household-based, controlled trial. Am J Clin Nutr. 2022 Mar 4;115(3):724-737. doi: 10.1093/ajcn/nqab379. PMID: 34792094; PMCID: PMC8895213. reference is the source of the parameter Zinc in Wheat New Delhi direct impact on health Sazawal S, et. al., (2018). Efficacy of high zinc biofortified wheat in improvement of micronutrient status, and prevention of morbidity among preschool children and women - a double masked, randomized, controlled trial. Nutr J. 2018 Sep 15;17(1):86. doi: 10.1186/s12937- 018-0391-5. PMID: 30219062; PMCID: PMC6139156. reference is the source of the parameter orange fleshed sweet potato MOzambique Hotz, C., et.al. (2012). A large-scale intervention to introduce orange sweet potato in rural Mozambique increases vitamin A intakes among children and women. British Journal of Nutrition, 108(1), 163-176. doi:10.1017/S00071145 11005174 reference is the source of the parameter http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ http://et.al/ 28 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters orange fleshed sweet potato Uganda Hotz C, et. al., (2012). Introduction of β- carotene-rich orange sweet potato in rural Uganda resulted in increased vitamin A intakes among children and women and improved vitamin A status among children. J Nutr. 2012 Oct;142(10):1871-80. doi: 10.3945/jn.111.151829. Epub 2012 Aug 8. PMID: 22875553. reference is the source of the parameter orange fleshed sweet potato Paul Van Jaarsveld et. al., (2005) β-Carotene- Rich Orange Fleshed Sweetpotato Improves the Vitamin A Status of Primary School Children Assessed by the Modified-Relative- Dose-Response Test June 2005American Journal of Clinical Nutrition 81(5):1080- 7Follow journal DOI: 10.1093/ajcn/81.5.1080 SourcePubMed reference is the source of the parameter orange fleshed sweet potato diarrhea children Kelly M. Jones, Alan de Brauw, (2015) Using Agriculture to Improve Child Health: Promoting Orange Sweet Potatoes Reduces Diarrhea, World Development, Volume 74, 2015, Pages 15-24, ISSN 0305- 750X,https://doi.org/10 .1016/j.worlddev.2015. 04.007. (https://www.sciencedi rect.com/science/articl e/pii/S0305750X150009 11) reference is the source of the parameter 29 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters orange fleshed sweet potato young children Low JW, Arimond M, Osman N, Cunguara B, Zano F, Tschirley D. A food-based approach introducing orange- fleshed sweet potatoes increased vitamin A intake and serum retinol concentrations in young children in rural Mozambique. J Nutr. 2007 May;137(5):1320-7. https://doi.org/10.1093 /jn/137.5.1320 PMID: 17449599. reference is the source of the parameter 30 EQUITY Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Outcome Remarks Poverty reduction, livelihood and jobs 01 Grower - More Yield (TPP) Grower: Higher yield --> Higher income / increased revenue for the producer --> reduced poverty User: Higher yield --> Higher supply --> Lower price --> reduced poverty Alston et al. (1995) chapter 4 method Alston et al. (1995) method Crop supply and demand elasticity IFPRI's IMPACT Economic surplus Producer and consumer surplus: relative yield increase � increased revenue + reduced prices � producer and consumer surplus Percentage of world market (Import/Expo rt) covered FAOSTAT (balanced) at market segment level (GLOMIP) Internal self- sufficiency rate FAOSTAT (balanced) at market segment level (GLOMIP) Production metrics Market Segment database at market segment level Technology parameters TPP database Economic surplus Alene et al. (2009, 2018) Alene et al. (2009, 2018) Poverty elasticity Literature (c/o Bert) People lifted out of poverty Economic surplus: economic surplus � people lifted out of poverty tru poverty elasticity Indirect effect of more yield AgGDP GloMIP | IO Np GloMIP | IO 31 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Outcome Remarks Economic surplus Weibe et al. (2021) Economic surplus * prevelance of poverty (decimal number, 0-1) Weibe et al. (2021) Poverty prevalence GloMIP | IO Adjusted Economic surplus "Scaled" Scaling / Portioning- out: scaled by undernourish ment and poverty � capture other impact areas indirectly Indirect effect of more yield; don't show the exact numbers Poverty reduction, livelihood and jobs 02 Grower - Less Loss/Risk - Climate Change 03 Grower - Less Loss/Risk - Biotic 04 Grower - Less Loss @ Location: cold, salinity and soil toxicity 05 Grower - Less loss - Other: mainly lodging Grower: Stable Production -> More stable revenue for people in non- optimal production areas (Income Stability) --> reduced poverty Index of improved income stability considering yield variation due to (a)biotic stresses, prevalence of CC, income levels, share of income from farming… Index will combine expert judgement and binary indicators considering weights. Production metrics Market Segment database at market segment level Income stability Technology parameters TPP database Stable production Income levels Share of income from farming Other variables (possibly weighted) Poverty reduction, livelihood and jobs 11 User - Processing quality & quantity Grower: higher price premium -- > higher income for producers --> reduced poverty Apply price premium in percentages to producer surplus. See economic surplus Producer surplus 32 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Outcome Remarks 12 User - Better nutrition: mainly Fe, Zn, Provit. A, B-carotene Link this to nutrition traits Price for regular and higher quality crops (price premium) Willingness to pay studies Poverty reduction, livelihood and jobs 07 Grower - Less labor/drudge ry 10 - User - Less labor Grower: Less farm work → more opportunity for other employment Less input cost → more profits Silva et al. (2018) - for rice Production metrics Market Segment database at market segment level Labour productivity Less farming drudgery → higher labour productivity Technology parameters TPP database Total labor (e.g. for rice) Labor productivity gross returns - total cost = net returns Total costs Net returns Total revenue Poverty reduction, livelihood and jobs + Gender and equality Economic surplus More production value is distributed disproportionatel ty across gender --> effect on gender equity Calculate people lifted out of povert by gender and then recalculate Gender Inequality Ratio Distribution of production benefits across gender Change in GIR NEEDS: (1) data on gender equity on agriculture; (2) Consult with crop focal persons for colelcting different information; (3) Distribution of benefits by gender; (4) Also 33 Impact Area Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Outcome Remarks check/take into account data on processors; (5) Disaggregati on of estimates: Male VS Female- heade households - > consults with Vivian Males and females in poverty Population lifted out of poverty Gender-inequality-weighted people lifted out of poverty Distribution of production benefits across gender Scaled population lifted out of poverty Males and females in poverty 34 PLANET Impact Area Outcome Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Remarks Climate adaptation Yield loss averted per stress 02 Grower - Less Loss/Risk - CC relevant; 04 Grower - Less Loss @ Location; 05 Grower - Less Loss - Other Abiotic stress tolerance --> avert yield loss J. Shi et al. ARGOS8 Variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol. J. 15, 207-216 (2017). X. Yin et al. CRISPR-Cas9 and CRISPR-Cpf1 mediated targeting of a stomatal developmental gene EPFL9 in rice. Plant Cell Rep. 36, 745-757 (2017). A. Zhang et al. Enhanced rice salinity tolerance via CRISPR/Cas9- targeted mutagenesis of the OsRR22 Gene. Mol. Breed. 39:47 (2019). Dose-response Will be estimated based on Incidence (Crop Growth Stage), Prevalence, Severity GloMIP (abiotic) Climate adaptation Yield loss averted per stress 03 Grower - Less Loss/Risk - Biotic Biotic stress resistance --> avert yield loss J.N. Tripathi et al. CRISPR/Cas9 Editing of endogenous banana streak virus in the B genome of Musa Spp. overcomes a major challenge in banana breeding. Dose-response Will be estimated based on Incidence (Crop Growth Stage), Prevalence, Severity https://pestdisp lace.org/ (biotic) 35 Impact Area Outcome Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Remarks Commun. Biol. 2, 1-11 (2019). N. Kumar et al. Further analysis of barley MORC1 using a highly efficient RNA- guided Cas9 Gene- editing system. Plant Biotechnol. J., 16, 1892-1903 (2018). M.A. Gomez et al. Simultaneous CRISPR/Cas9- mediated editing of cassava EIF4E isoforms NCBP- 1 and NCBP-2 reduces cassava brown streak disease symptom severity and incidence. Plant Biotechnol. J., 17, 421-434 (2019). A.V. Makhotenko et al. Functional analysis of coilin in virus resistance and stress tolerance of potato solanum tuberosum using CRISPR-Cas9 editing. Biochem. Biophys. 484, 88- 91 (2019). J. Zhou et al. Gene targeting by the TAL effector PthXo2 reveals cryptic resistance gene for bacterial blight of rice. Plant J. 82, 632-643 (2015). Y. Wang et al. Simultaneous 36 Impact Area Outcome Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Remarks editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat. Biotechnol. 32, 947-951 (2014). by crop, stress type, location Climate mitigation Reduction in GHG emissions More yield (y) More yield –> less GHG per ha, land sparing H. Gao et al. Superior field performance of waxy corn engineered using CRISPR–Cas9. Nat. Biotechnol. 38, 579–581 (2020). J. Zhou et al. Multiplex QTL Editing of grain- related genes improves yield in elite rice varieties. Plant Cell Rep. 38, 475-485 (2019). W. Wang et al. Transgenerational CRISPR-Cas9 activity facilitates multiplex gene editing in allopolyploid wheat. CRISPR J. 1, 65-74 (2018). Dose-response Will be estimated Reduction in GHG emissions Nutrient use efficiency (n) Greater NUE -> less GHG B. Hu et al. Genetic improvement toward nitrogen- use efficiency in rice: Lessons and perspectives. Molecular Plant. 16. 64-74 (2023). Dose-response Will be estimated nutrient use amount kg/ha by crop and location Reduction in GHG emissions Maturity time (m) Early maturity – less paddy X. Li et al. High- efficiency Dose-response Will be estimated current days, new 37 Impact Area Outcome Inputs (i.e. trait, outcome from different impact area) Impact Pathway (IP) Useful references for IP Methods Useful references for methods Parameters References for parameters Remarks submergence time under water – less methane breeding of early- maturing rice cultivars via CRISPR/Cas9- mediated genome editing. J. Genet. Genomics, 44, 175-178 (2017). days/current days Reduction in GHG emissions Cooking energy time (c) Less time to cook --> energy saving, less GHG Dose-response Will be estimated Reduction in GHG emissions Area under crop (a) area under crop - deforestation - impact on GHG Dose-response Will be estimated Environmental health and biodiversity Reduction in inputs use (RIU) (%) More resilience to biotic and abiotic stress More resilience to biotic and abiotic stresses – less pesticide and chemical use Dose-response will be stimated on the basos of : Stress area/prevalence (p) Inputs are Reduction in inputs use (RIU) (%) Higher NUE Higher NUE – less fertilizer, less water, less groundwager extraction Dose-response Stress time/incidence (i) herbicides, by crop, stress type, location Reduction in inputs use (RIU) (%) Dose-response Severity of stress (s) fertilizers stresses: drought, flood, salinity, BLB, blast, etc Reduction in inputs use (RIU) (%) Dose-response Damage from stress (d) water Reduction in inputs use (RIU) (%) pesticides 38 MVM 1. Climate change adaptation 𝑌𝐿𝐴!",$,% = (𝑝 × 𝑖 × 𝑑 × 𝑠𝑒 × 𝑝𝑟)!",$,% + 𝜀!",$,% where: 𝑌𝐿𝐴 = yield loss averted (%) 𝑝 = prevalence 𝑖 = incidence 𝑑 = damage 𝑠𝑒 = severity 𝑝𝑟 = probability of stress by: 𝑐𝑟 = crop, 𝑙 = location and 𝑠 = stress Climate change is projected to have negative impacts on crop productivity, inter alia, through extreme weather events, such as droughts, floods, heatwaves, as well as evolving pest and disease environments, and saline seawater intrusion. Therefore, adapting to climate change through genetic innovation requires the development of new crop varieties with higher biotic and abiotic stress resistance. The key variable capturing climate change adaptation in this MVM is yield loss averted due to varietal improvement despite being exposed to these climate change hazards. Using statistical analyses (dose-response models) of historical data on crop yields, weather extremes (droughts, heat waves, extreme precipitation events), as well as review of literature on the impacts of salinity, crop pests and diseases, we aim to estimate the impact coefficients of these abiotic and biotic stresses on crop yields. These coefficients will allow estimating the impact of varietal improvement on averting yield losses under changing climate. The ultimate goal of this MVM is to create a matrix of coefficients linking varietal improvement characteristics to yield losses averted despite climate change hazards. These coefficients will serve as inputs for evaluating the investment returns from varietal improvement within the Investment Dashboard. MVM 2. Climate change mitigation ∆𝐺𝐻𝐺!",$ = ∆𝑦!",$ + ∆𝑛!",$ + 𝑚!",$ + 𝑐!",$ + ∆𝑎!",$ + 𝜀!",$ where: 𝑦 = yield 𝑛 = nutrient use efficiency (nutrient use amount, kg/ha) 𝑚 = maturity time (current days, new days/current days) 𝑐 = cooking energy time 𝑎 = area under the crop by: 𝑐𝑟 = crop and 𝑙 = location 39 Food systems are currently generating up to one third of global Greenhouse Gas (GHG) emissions. In particular, methane emissions from paddy rice production alone account for about 10% of agricultural CO2-equivalent emissions. Therefore, genetic innovation needs to play a fundamental role in helping reduce the carbon footprint from crop production and consumption. There are several impact pathways to achieve this. Firstly, increasing nutrient use efficiency means lower fertilizer application for the same crop yield. Secondly, decreasing maturity period of crops, particularly rice, will lead to fewer days being under flooded irrigation conditions, and hence, less methane emissions. Thirdly, higher crop yields may avert cropland expansion and prevent land use changes associated with higher GHG emissions. Finally, crop varieties which take less time in cooking need less energy for their culinary preparation thus contributing to reducing GHG emissions from the food systems. We follow a methodological approach of dose-response modelling using the historical data on GHG emissions from crops associating these to crop yields, areas under crops, and fertilizer use, as well as the review of literature on the links from crop maturity times and cooking times to GHG emissions to establish a matrix of coefficients linking varietal improvement characteristics to lower GHG emissions. These coefficients will serve as inputs for evaluating the investment returns from varietal improvement for climate change mitigation within the Investment Dashboard. MVM 3. Environmental health and biodiversity 𝑅𝐼𝑈!",$,&' = (𝑝 × 𝑖 × 𝑑 × 𝑠𝑒 × 𝑝𝑟)!",$,&' + 𝜀!",$,&' where: 𝑅𝐼𝑈 = reduction in inputs use (%) 𝑝 = prevalence 𝑖 = incidence 𝑑 = damage 𝑠𝑒 = severity 𝑝𝑟 = probability of stress by: 𝑐𝑟 = crop, 𝑙 = location and 𝑖𝑛 = input Excessive uses of fertilizers, pesticides, and herbicides in crop production in many parts of the world are currently leading to pollution of surface- and groundwaters, long-lasting damages to biodiversity, e.g. loss of pollinators and reduction in soil microorganisms essential to maintain soil fertility. Overuse of water for crop irrigation is leading to increasing competition for water between human needs and ecosystems requirements, depletion of groundwater aquifers, and decreasing water quality. Genetic innovations that help reduce the need for input applications (pesticides, herbicides, fertilizers, water) in crop production will thus directly contribute to environmental health and biodiversity protection. Our target variable in this MVM is reduction in input use. We will evaluate how each TPP will contribute to lower input use. Lower input use will lead to cost savings, but even more importantly in this context to improved environmental health and biodiversity. We will seek to identify the relationships between lower input use 40 and environmental health and biodiversity based on the review of the literature. In this regard, estimating the investment returns means valuating these environmental health and biodiversity impacts in addition to cost savings. We will use the Total Economic Value (TEV) framework and the relevant economic values from the Ecosystem Services Valuation Database (ESVD) to estimate these environmental health and biodiversity values in monetary terms. 5.6. Refinement of the MVMs (Future ac3vity) The team will refine the model based on state-of-the art literature. Pilot studies will be conducted using the refined models and data from SPMSD, Breeding Portal, GloMIP and breeding pipelines. 41 6 List of par8cipants Photo 6. Work Package 4 – Pipeline Investment Case Workshop Participants ANDRADE, Robert Santiago Postdoctoral Fellow Alliance of Bioversity and CIAT Palmira, Colombia r.s.andrade@cgiar.org BANZIGER, Marianne (online) International Maize and Wheat Improvement Center Texcoco, Mexico mariannebanziger@outlook.com BAYOT, Ruvicyn Associate Manager – Project Coordination International Rice Research Institute Los Baños, Laguna, Philippines r.bayot@irri.org BOY, Erick Nutrition Research Coordinator International Food Policy Research Institute/ Harvest Plus Washington DC, USA e.boy@cgiar.org CABRERA, Ellanie Assistant Scientist – Agricultural Economics International Rice Research Institute Los Baños, Laguna, Philippines e.cabrera@irri.org mailto:r.s.andrade@cgiar.org mailto:mariannebanziger@outlook.com mailto:r.bayot@irri.org mailto:e.boy@cgiar.org mailto:e.cabrera@irri.org 42 DEMONT, Matty Principal Scientist – Market and Food Systems Research International Rice Research Institute Los Baños, Laguna, Philippines m.demont@irri.org DIKITANAN, Rowell Associate Scientist – Impact Evaluation, Policy and Foresight International Rice Research Institute Los Baños, Laguna, Philippines r.dikitanan@irri.org DOCTOLERO, Anna Christine Officer – Administrative Coordination International Rice Research Institute Los Baños, Laguna, Philippines c.doctolero@irri.org GBEGBELEGBE, Sika (online) Agricultural Economist International Institute for Tropical Agriculture Lilongwe, Malawi s.gbegbelegbe@cgiar.org HAREAU, Guy Principal Scientist International Potato Center Lima, Peru g.hareau@cgiar.org LABORTE, Alice Senior Scientist II – Geospatial Scientist International Rice Research Institute Los Baños, Laguna, Philippines a.g.laborte@irri.org LENAERTS, Bert Postdoctoral Fellow – Agricultural and Resource Economics International Rice Research Institute Los Baños, Laguna, Philippines b.lenaerts@irri.org MAUNAHAN, Aileen Assistant Scientist – Remote Sensing International Rice Research Institute Los Baños, Laguna, Philippines a.maunahan@irri.org MIRZABAEV, Alisher Senior Scientist I – Policy analysis/ Climate change International Rice Research Institute Los Baños, Laguna, Philippines a.mirzabaev@irri.org PAGUIRIGAN, Neale Marvin Specialist – Information Systems International Rice Research Institute Los Baños, Laguna, Philippines n.paguirigan@irri.org PLATTEN, John Senior Scientist II – Breeding Innovation International Rice Research Institute Los Baños, Laguna, Philippines j.platten@irri.org PEDE, Valerien Senior Scientist II – Agricultural Economics International Rice Research Institute Los Baños, Laguna, Philippines v.pede@irri.org mailto:m.demont@irri.org mailto:r.dikitanan@irri.org mailto:c.doctolero@irri.org mailto:s.gbegbelegbe@cgiar.org mailto:g.hareau@cgiar.org mailto:a.g.laborte@irri.org mailto:b.lenaerts@irri.org mailto:a.maunahan@irri.org mailto:a.mirzabaev@irri.org mailto:n.paguirigan@irri.org mailto:v.pede@irri.org 43 RICARTE, Phoebe Associate Scientist – Agricultural Economics International Rice Research Institute Los Baños, Laguna, Philippines p.ricarte@irri.org TWINE, Edgar (online) Agricultural Economist AfricaRice Center Kampala, Uganda e.twine@cgiar.org VALERA, Harold Scientist II – Economic Modelling International Rice Research Institute Los Baños, Laguna, Philippines h.valera@irri.org VILLANUEVA, Donald Senior Associate Scientist I – Agricultural Economics International Rice Research Institute Los Baños, Laguna, Philippines d.villanueva@irri.org YNION, Jhoanne Associate Scientist – Market Research International Rice Research Institute Los Baños, Laguna, Philippines j.ynion@irri.org mailto:p.ricarte@irri.org mailto:e.twine@cgiar.org mailto:h.valera@irri.org mailto:d.villanueva@irri.org 44