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Ramirez-Villegas, J.

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Ramirez-Villegas
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Ramirez-Villegas, J.
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0000-0002-8044-583X

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2018 - 201922020 - 20245
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    Climate change impact and adaptation of rainfed cereal crops in sub-Saharan Africa
    (Elsevier, 2024) Alimagham, S.; Van Loon, M.P.; Ramirez-Villegas, J.; Adjei-Nsiah, S.; Baijukya, F.; Bala, A.; Chikowo, R.; Silva, J.V.; Abdelkader Mahamane Soulé; Taulya, G.; Tenorio, F.A.; Tesfaye, K.; Ittersum, M.K. van
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    Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products
    (National Academy of Sciences, 2023) Etten, J. van; de Sousa, K.; Cairns, J.E.; Dell'acqua, M.; Fadda, C.; Güereña, D.T.; Heerwaarden, J. van; Assefa, T.; Manners, R.; Müller, A.; Pè, M.E.; Polar, V.; Ramirez-Villegas, J.; Solberg, S.Ø.; Teeken, B.; Tufan, H.A.
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    State of ex situ conservation of landrace groups of 25 major crops
    (Nature Publishing Group, 2022) Ramirez-Villegas, J.; Khoury, C.K.; Achicanoy, H.; Diaz, M.V.; Mendez, A.C.; Sosa, C.C.; Kehel, Z.; Guarino, L.; Abberton, M.; Aunario, J.; Awar, B.A.; Alarcon, J.C.; Amri, A.; Anglin, N.L.; Azevedo, V.; Aziz, K.; Capilit, G.L.; Chavez, O.; Chebotarov, D.; Costich, D.E.; Debouck, D.; Ellis, D.; Falalou, H.; Fiu, A.; Ghanem, M.E.; Giovannini, P.; Goungoulou, A.J.; Gueye, B.; Hobyb, A.I.E.; Jamnadass, R.; Jones, C.S.; Kpeki, B.; Lee, J.S.; McNally, K.; Muchugi, A.; Ndjiondjop, M.N.; Oyatomi, O.; Payne, T.S.; Ramachandran, S.; Rossel, G.; Roux, N.; Ruas, M.; Sansaloni, C.; Sardos, J.; Setiyono, T.; Tchamba, M.; van den Houwe, I.; Velazquez, J.A.; Venuprasad, R.; Wenzl, P.; Yazbek, M.; Zavala Espinosa, C.
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    One CGIAR and the Integrated Agri-food Systems Initiative: from short-termism to transformation of the world's food systems
    (Public Library of Science, 2021) Govaerts, B.; Negra, C.; Camacho Villa, T.C.; Chavez, X.; Diaz Espinosa, A.; Fonteyne, S.; Gardeazabal, A.; González, G.; Singh, R.G.; Kommerell, V.; Kropff, W.; Lopez-Saavedra, V.; Mena-Lopez, G.; Odjo, S.; Palacios-Rojas, N.; Ramirez-Villegas, J.; Van Loon, J.; Vega, D.; Verhulst, N.; Woltering, L.; Jahn, M.; Kropff, Martinus
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    CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate
    (Crop Science Society of America (CSSA), 2020) Ramirez-Villegas, J.; Molero Milan, A.; Alexandrov, N.; Asseng, S.; Challinor, A.; Crossa, J.; Van Eeuwijk, F.A.; Ghanem, M.E.; Grenier, C.; Heinemann, A.B.; Jiankang Wang; Juliana, P.; Kehel, Z.; Kholova, J.; Koo, J.; Pequeno, D.N.L.; Quiroz, R.; Rebolledo, C.; Sukumaran, S.; Vadez, V.; White, J.W.; Reynolds, M.P.
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    A framework for priority-setting in climate smart agriculture research
    (Elsevier, 2018) Thornton, P.; Whitbread, A.; Baedeker, T.; Cairns, J.E.; Claessens, L.; Baethgen, W.; Bunn, C.; Friedmann, M.; Giller, K.E.; Herrero, M.; Howden, M.; Kilcline, K.; Nangia, V.; Ramirez-Villegas, J.; Shalander Kumar; West, P.C.; Keating, B.
    Climate-smart agriculture (CSA) is widely promoted as an approach for reorienting agricultural development under the realities of climate change. Prioritising research-for-development activities is crucial, given the need to utilise scarce resources as effectively as possible. However, no framework exists for assessing and comparing different CSA research investments. Several aspects make it challenging to prioritise CSA research, including its multi-dimensional nature (productivity, adaptation and mitigation), the uncertainty surrounding many climate impacts, and the scale and temporal dependencies that may affect the benefits and costs of CSA adoption. Here we propose a framework for prioritising agricultural research investments across scales and review different approaches to setting priorities among agricultural research projects. Many priority-setting case studies address the short- to medium-term and at relatively local scales. We suggest that a mix of actions that span spatial and temporal time scales is needed to be adaptive to a changing climate, address immediate problems and create enabling conditions for enduring change.
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    The climate-smart village approach: framework of an integrative strategy for scaling up adaptation options in agriculture
    (Resilience Alliance, 2018) Aggarwal, P.K.; Jarvis, A.; Campbell, B.M.; Zougmore, R.B.; Khatri-Chhetri, A.; Vermeulen, S.; Loboguerrero, A.M.; Sebastian, L.; Kinyangi, J.; Bonilla-Findji, O.; Radeny, M.; Recha, J.; Martínez Barón, D.; Ramirez-Villegas, J.; Huyer, S.; Thornton, P.; Wollenberg, E.; Hansen, J.W.; Alvarez Toro, P.; Aguilar Ariza, A.; Arango Londoño, D.; Patiño Bravo, V.; Rivera, O.; Ouédraogo, M.; Bui Tan Yen
    Increasing weather risks threaten agricultural production systems and food security across the world. Maintaining agricultural growth while minimizing climate shocks is crucial to building a resilient food production system and meeting developmental goals in vulnerable countries. Experts have proposed several technological, institutional, and policy interventions to help farmers adapt to current and future weather variability and to mitigate greenhouse gas (GHG) emissions. This paper presents the climate-smart village (CSV) approach as a means of performing agricultural research for development that robustly tests technological and institutional options for dealing with climatic variability and climate change in agriculture using participatory methods. It aims to scale up and scale out the appropriate options and draw out lessons for policy makers from local to global levels. The approach incorporates evaluation of climate-smart technologies, practices, services, and processes relevant to local climatic risk management and identifies opportunities for maximizing adaptation gains from synergies across different interventions and recognizing potential maladaptation and trade-offs. It ensures that these are aligned with local knowledge and link into development plans. This paper describes early results in Asia, Africa, and Latin America to illustrate different examples of the CSV approach in diverse agroecological settings. Results from initial studies indicate that the CSV approach has a high potential for scaling out promising climate-smart agricultural technologies, practices, and services. Climate analog studies indicate that the lessons learned at the CSV sites would be relevant to adaptation planning in a large part of global agricultural land even under scenarios of climate change. Key barriers and opportunities for further work are also discussed.
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