Campbell, B. M. et al. Reducing risks to food security from climate change. Glob. Food Secur. 11, 34–43 (2016).
Nair, P. K. R. Grand challenges in agroecology and land use systems. Front. Environ. Sci. 2, 1 (2014).
Connolly-Boutin, L. & Smit, B. Climate change, food security, and livelihoods in sub-Saharan Africa. Reg. Environ. Change 16, 385–399 (2016).
Maxwell, D. The political economy of urban food security in Sub-Saharan Africa. World Dev. 27, 1939–1953 (1999).
IPCC. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds. Shukla, P. R. et al.) (2019).
Altieri, M. A., Nicholls, C. I., Henao, A. & Lana, M. A. Agroecology and the design of climate change-resilient farming systems. Agron. Sust. Dev. 35, 869–890 (2015).
Tadross, M. et al. Growing-season rainfall and scenarios of future change in southeast Africa: Implications for cultivating maize. Clim. Res. 40, 147–161 (2009).
Challinor, A., Wheeler, T., Garforth, C., Craufurd, P. & Kassam, A. Assessing the vulnerability of food crop systems in Africa to climate change. Clim. Change 83, 381–399 (2007).
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. USA 114, 9326–9331 (2017).
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).
Hammond, S. T. et al. Food spoilage, storage, and transport: Implications for a sustainable future. Bioscience 65, 758–768 (2015).
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
Newton, A. C., Johnson, S. N. & Gregory, P. J. Implications of climate change for diseases, crop yields and food security. Euphytica 179, 3–18 (2011).
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
Lee, J. G. & Kang, M. Geospatial big data: Challenges and opportunities. Big Data Res. 2, 74–81 (2015).
Serra-Diaz, J. M. & Franklin, J. What’s hot in conservation biogeography in a changing climate? Going beyond species range dynamics. Divers. Distrib. 25, 492–498 (2019).
Snyder, K. A., Miththapala, S., Sommer, R. & Braslow, J. The yield gap: Closing the gap by widening the approach. Exp. Agric. 53, 445–459 (2017).
Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).
Fernández, M., Hamilton, H. & Kueppers, L. M. Characterizing uncertainty in species distribution models derived from interpolated weather station data. Ecosphere 4, 1–17 (2013).
Grabowski, P. et al. Assessing adoption potential in a risky environment: The case of perennial pigeonpea. Agric. Syst. 171, 89–99 (2019).
Habib-Mintz, N. Biofuel investment in Tanzania: Omissions in implementation. Energy Policy 38, 3985–3997 (2010).
Shiferaw, B. A., Okello, J. & Reddy, R. V. Adoption and adaptation of natural resource management innovations in smallholder agriculture: Reflections on key lessons and best practices. Environ. Dev. Sustain. 11, 601–619 (2009).
Kwesiga, F., Akinnifesi, F. K., Mafongoya, P. L., McDermott, M. H. & Agumya, A. Agroforestry research and development in southern Africa during the 1990s: Review and challenges ahead. Agrofor. Syst. 59, 173–186 (2003).
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).
Fischer, G. et al. Global agro-ecological zones (GAEZ v3. 0)-model documentation. In International Institute for Applied Systems Analysis/Food and Agriculture Organization of the United Nations (2012).
Heal, G. & Millner, A. Reflections: Uncertainty and decision making in climate change economics. Rev. Environ. Econ. Policy 8, 120–137 (2014).
Harth, A., Knoblock, C. A., Stadtmüller, S., Studer, R. & Szekely, P. On-the-fly integration of static and dynamic linked data. In Proceedings of the Fourth International Workshop on Consuming Linked Data (2013).
Ginige, A., Javadi, B., Calheiros, R. N. & Hendriks, S. L. A smart computing framework centered on user and societal empowerment to achieve the sustainable development goals. In International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas (eds. Bassioni, G., Kebe, C. M. F., Gueye, A. & Ndiaye, A.) 158–172 (Springer, Cham, 2019).
Ramirez-Cabral, N. Y. Z., Kumar, L. & Taylor, S. Crop niche modeling projects major shifts in common bean growing areas. Agric. For. Meteorol. 218, 102–113 (2016).
Mejias, P., & Piraux, M. AquaCrop, the crop water productivity model. In Food and Agriculture Organization of the United Nations (2017).
Hijmans, R. J., Guarino, L., Cruz, M. & Rojas, E. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl. 127, 15–19 (2001).
Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265 (2003).
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. & Freebairn, D. M. APSIM: A novel software system for model development, model testing, and simulation in agricultural systems research. Agric. Syst. 50, 255–271 (1996).
Dragićević, S. The potential of Web-based GIS. J. Geogr. Syst. 6, 79–81 (2004).
Kraak, M. J. The role of the map in a Web-GIS environment. J. Geogr. Syst. 6, 83–93 (2004).
Moore, R. Introducing Google Earth Engine. The Official google.org blog https://blog.google.org/2010/12/introducing-google-earth-engine_57.html (2010).
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Agapiou, A. Remote sensing heritage in a petabyte-scale: Satellite data and heritage Earth Engine© applications. Int. J. Digit. Earth. 10, 82–102 (2017).
HarvestChoice-International Food Policy Research Institute (IFPRI). Agro-Ecological Zones for Africa South of the Sahara V3. Harvard Dataverse https://doi.org/10.7910/DVN/M7XIUB (2015).
Kane, D. A., Roge, P. & Snapp, S. S. A systematic review of perennial staple crops literature using topic modeling and bibliometric analysis. PLoS ONE 11, e0155788 (2016).
Thornton, P. K. & Herrero, M. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nat. Clim. Change. 5, 830–836 (2015).
Mayes, S. et al. The potential for underutilized crops to improve security of food production. J. Exp. Bot. 63, 1075–1079 (2012).
Peter, B. G., Mungai, L. M., Messina, J. P. & Snapp, S. S. Nature-based agricultural solutions: Scaling perennial grains across Africa. Environ. Res. 159, 283–290 (2017).
Hannah, L. et al. Global climate change adaptation priorities for biodiversity and food security. PLoS ONE 8, e72590 (2013).
Snapp, S. S., Blackie, M. J., Gilbert, R. A., Bezner-Kerr, R. & Kanyama-Phiri, G. Y. Biodiversity can support a greener revolution in Africa. Proc. Natl. Acad. Sci. USA 107, 20840–20845 (2010).
Sanchez, P. A. Soil fertility and hunger in Africa. Science 295, 2019–2020 (2002).
Foyer, C. H. et al. Neglecting legumes has compromised human health and sustainable food production. Nat. Plants 2, 16112 (2016).
Kole, C. et al. Application of genomics-assisted breeding for generation of climate resilient crops: Progress and prospects. Front. Plant Sci. 6, 563 (2015).
Sinha, P. et al. 2016 Identification and validation of selected universal stress protein domain containing drought-responsive genes in Pigeonpea (Cajanus cajan L.). Front. Plant Sci. 6, 1065 (2016).
Choudhary, A. K., Sultana, R., Pratap, A., Nadarajan, N. & Jha, U. C. Breeding for abiotic stresses in pigeonpea. J. Food Legum. 24, 165–174 (2011).
Ehlers, J. D. & Hall, A. E. Cowpea (Vigna unguiculata L. walp.). Field Crops Res. 53, 187–204 (1997).
De Ron, A. M. et al. 2019 Common bean genetics, breeding, and genomics for adaptation to changing to new agri-environmental conditions. In Genomic Designing of Climate-Smart Pulse Crops (ed. Kole, C.) 1–106 (Springer, Cham, 2019).
Smýkal, P. et al. Legume crops phylogeny and genetic diversity for science and breeding. Crit. Rev. Plant Sci. 34, 43–104 (2015).
Snapp, S. S., Cox, C. M. & Peter, B. G. Multipurpose legumes for smallholders in sub-Saharan Africa: Identification of promising ‘scale out’ options. Glob. Food Secur. 23, 22–32 (2019).
Ramírez-Villegas, J. & Thornton, P. K. Climate change impacts on African crop production. In CCAFS Working Paper No. 119. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (2015).
Robertson, C. C. Black, white, and red all over: Beans, women, and agricultural imperialism in twentieth-century Kenya. Agric. Hist. 71, 259–299 (1997).
Rusinamhodzi, L., Corbeels, M., Nyamangara, J. & Giller, K. E. Maize–grain legume intercropping is an attractive option for ecological intensification that reduces climatic risk for smallholder farmers in central Mozambique. Field Crops Res. 136, 12–22 (2012).
Bezner-Kerr, R., Snapp, S., Chirwa, M., Shumba, L. & Msachi, R. Participatory research on legume diversification with Malawian smallholder farmers for improved human nutrition and soil fertility. Exp. Agric. 43, 437–453 (2007).
Jones, A. D., Shrinivas, A. & Bezner-Kerr, R. Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy 46, 1–12 (2014).
Ojiewo, C. et al. The role of vegetables and legumes in assuring food, nutrition, and income security for vulnerable groups in Sub-Saharan Africa. World Med. Health Policy 7, 187–210 (2015).
Wood, S., Sebastian, K., Nachtergaele, F., Nielsen, D. & Dai, A. Spatial aspects of the design and targeting of agricultural development strategies. In Environment and Production Technology Division, International Food Policy Research Institute, Washington, DC, EPTD Discussion Paper No. 44 (1999).
Chivenge, P., Mabhaudhi, T., Modi, A. T. & Mafongoya, P. The potential role of neglected and underutilised crop species as future crops under water scarce conditions in Sub-Saharan Africa. International Journal of Environmental Research and Public Health 12, 5685–5711 (2015).
Dakora, F. D. Biogeographic distribution, nodulation and nutritional attributes of underutilized indigenous African legumes. In II International Symposium on Underutilized Plant Species: Crops for the Future-Beyond Food Security, 53–64 International Society for Horticultural Science, ISHS Acta Horticulturae 979 (2011).
Traub, J. et al. Screening for heat tolerance in Phaseolus spp. using multiple methods. Crop Sci. 58, 2459–2469 (2018).
Knight, A. T. et al. Knowing but not doing: Selecting priority conservation areas and the research–implementation gap. Conserv. Biol. 22, 610–617 (2008).
Funk, C. et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).
Vizy, E. K., Cook, K. H., Chimphamba, J. & McCusker, B. Projected changes in Malawi’s growing season. Clim. Dyn. 45, 1673–1698 (2015).
Jayanthi, H. et al. Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study. Int. J. Disast. Risk Res. 4, 71–81 (2013).
FAO. ECOCROP, Crop Environmental Requirements Database. Food and Agriculture Organization of the United Nations (1991).
Peter, B. G., Messina, J. P. & Lin, Z. Web-based GIS for spatiotemporal crop climate niche mapping https://doi.org/10.7910/DVN/UFC6B5,HarvardDataverse,V2 (2019).
Beebe, S. et al. Genetic improvement of common beans and the challenges of climate change. In Crop Adaptation to Climate Change (eds. Yadav, S. S., Redden, R. J., Hatfield, J. L., Lotze-Campen, H. & Hall, A. E.) Ch. 16, 356–369 (Wiley-Blackwell, 2011).
de Jong, R. & de Bruin, S. Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences 9, 71–77 (2012).
Swist, T. & Magee, L. Academic publishing and its digital binds: Beyond the paywall towards ethical executions of code. Cult.s Unbound J. Curr. Cult. Res. 9, 240–259 (2018).
Hedding, D. W. Comments on “Factors affecting global flow of scientific knowledge in environmental sciences” by Sonne et al. (2020). Sci. Total Environ. 705, 135933 (2020).
Rippke, U. et al. Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nat. Clim. Change 6, 605–609 (2016).
Sinclair, T. R., Marrou, H., Soltani, A., Vadez, V. & Chandolu, K. C. Soybean production potential in Africa. Glob. Food Secur. 3, 31–40 (2014).
Hajjarpoor, A. et al. Characterization of the main chickpea cropping systems in India using a yield gap analysis approach. Field Crops Res. 223, 93–104 (2018).
Ortega, D. L., Waldman, K. B., Richardson, R. B., Clay, D. C. & Snapp, S. Sustainable intensification and farmer preferences for crop system attributes: Evidence from Malawi’s central and southern regions. World Dev. 87, 139–151 (2016).
Simtowe, F., Asfaw, S. & Abate, T. Determinants of agricultural technology adoption under partial population awareness: The case of pigeonpea in Malawi. Agric. Food Econ. 4, 7 (2016).
Norris, K. Agriculture and biodiversity conservation: Opportunity knocks. Conservation Letters 1, 2–11 (2008).
Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Change 6, 810–813 (2016).
Pekel, J., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Allen, R. G. et al. EEFlux: A Landsat-based evapotranspiration mapping tool on the Google Earth Engine. In 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation-A Tribute to the Career of Terry Howell, Sr. Conference Proceedings, 1–11. American Society of Agricultural and Biological Engineers (2015).
Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day L3 global 1km SIN grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MOD11A2.006 (2015).
Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land Process.. DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).
Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+aqua land cover type yearly L3 global 500m SIN grid V006. NASA EOSDIS Land Process. DAAC https://doi.org/10.5067/MODIS/MCD12Q1.006 (2019).
Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
Teluguntla, P. G. et al. Global cropland area database (GCAD) derived from remote sensing in support of food security in the twenty-first century: Current achievements and future possibilities. In Remote Sensing Handbook, Land Resources: Monitoring, Modelling, and Mapping Vol 2, Ch. 7 (CRC Press, 2015).
Arino, O., Ramos, J. R., Kalogirou, V., Defourny, P. & Achard, F. GlobCover 2009. In ESA Living Planet Symposium 1–3. European Space Agency (2010).
Hengl, T. & MacMillan, R. A. Predictive Soil Mapping with R, https://www.soilmapper.org (OpenGeoHub foundation, Wageningen, The Netherlands, 2019).
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
Rossel, R. A. V. & Bouma, J. Soil sensing: A new paradigm for agriculture. Agric. Syst. 148, 71–74 (2016).
Herrick, J. E. et al. The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing. J. Soil Water Conserv. 68, 5A-12A (2013).
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change. 9, 758–763 (2019).
ESRI. ArcGIS Desktop: Release 10.8. (Environmental Systems Research Institute, CAs, 2020).
Source: Ecology - nature.com
