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Food

Assessing and addressing the global state of food production data scarcity

techbalu06By techbalu06February 20, 2024No Comments22 Mins Read

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  • Loizou, E., Karelakis, C., Galanopoulos, K. & Mattas, K. The role of agriculture as a development tool for a regional economy. Agric. Syst. 173, 482–490 (2019).

    Article 

    Google Scholar 

  • Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Carpenter, S. R., Booth, E. G. & Kucharik, C. J. Extreme precipitation and phosphorus loads from two agricultural watersheds. Limnol. Oceanogr. 63, 1221–1233 (2018).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Willett, W. et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Zhang, J., Wang, S., Zhao, W., Meadows, M. E. & Fu, B. Finding pathways to synergistic development of Sustainable Development Goals in China. Humanit. Soc. Sci. Commun. 9, 21 (2022).

    Article 

    Google Scholar 

  • Searchinger, T. et al. Creating a Sustainable Food Future. World Resources Report 2013–14: Interim Findings (World Resources Institute, 2020).

  • Hoekstra, A. Y. & Mekonnen, M. M. The water footprint of humanity. Proc. Natl Acad. Sci. USA 109, 3232–3237 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Saleem, M. Possibility of utilizing agriculture biomass as a renewable and sustainable future energy source. Heliyon 8, e08905 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Agricultural Model Intercomparison and Improvement Project (AgMIP) https://doi.org/10.15482/USDA.ADC/1212378 (2015).

  • Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Becker-Reshef, I., Justice, C., Whitcraft, A. K. & Jarvis, I. GEOGLAM: a GEO initiative on global agricultural monitoring. In IGARSS 2018 — 2018 IEEE International Geoscience and Remote Sensing Symposium 8155–8157 (2018).

  • Sellitti, S. Evaluation of CGIAR Platform for Big Data in Agriculture (CGIAR, 2021).

  • Yu, Q. et al. A cultivated planet in 2010 — part 2: the global gridded agricultural-production maps. Earth Syst. Sci. Data 12, 3545–3572 (2020).

    Article 
    ADS 

    Google Scholar 

  • Fischer, G. et al. Global Agro-Ecological Zones v4 — Model Documentation (IIASA/FAO, 2021).

  • Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000 — global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).

  • Weersink, A., Fraser, E., Pannell, D., Duncan, E. & Rotz, S. Opportunities and challenges for big data in agricultural and environmental analysis. Annu. Rev. Resour. Econ. 10, 19–37 (2018).

    Article 

    Google Scholar 

  • Global Review of Agricultural Census Methodologies and Results (2006–2015) World Programme for the Census of Agriculture 2010 (FAO, 2021).

  • FAOSTAT Statistical Database (FAO, 2024); https://www.fao.org/faostat/en/#data.

  • Conducting Agricultural Censuses and Surveys FAO Statistical Development Series No. 6) (Food and Agriculture Organization of the United Nations, 1996); https://www.fao.org/economic/the-statistics-division-ess/world-census-of-agriculture/conducting-of-agricultural-censuses-and-surveys/en/.

  • Statistical Office of the European Union (EUROSTAT, 2023); https://ec.europa.eu/eurostat.

  • Lahti, L., Huovari, J., Kainu, M. & Biecek, P. Retrieval and analysis of Eurostat open data with the eurostat package. The R Journal 9, 385–392 (2017).

    Article 

    Google Scholar 

  • World Programme For The Census Of Agriculture 2020 Vol. 1 (FAO, 2017).

  • Maria, D., Michele, M. & Felix, R. Development of a National and Sub-National Crop Calendars Data Set Compatible with Remote Sensing Derived Land Surface Phenology (European Union, 2018).

  • Fritz, S. et al. A comparison of global agricultural monitoring systems and current gaps. Agric. Syst. 168, 258–272 (2019).

    Article 

    Google Scholar 

  • Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Article 

    Google Scholar 

  • Becker-Reshef, I. et al. Crop type maps for operational global agricultural monitoring. Sci. Data 10, 172 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kotsuki, S. & Tanaka, K. SACRA — a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI. Hydrol. Earth Syst. Sci. 19, 4441–4461 (2015).

    Article 
    ADS 

    Google Scholar 

  • Laborte, A. G. et al. RiceAtlas, a spatial database of global rice calendars and production. Sci. Data 4, 170074 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • See, L. et al. Improved global cropland data as an essential ingredient for food security. Glob. Food Secur. 4, 37–45 (2015).

    Article 

    Google Scholar 

  • Global Strategy to Improve Agricultural and Rural Statistics: Report of the Friends of the Chair on Agricultural Statistics (World Bank, 2010).

  • Independent External Evaluation of the United Nations Food and Agricultural Organization (Food and Agricultural Organization of the United Nations, 2005); https://www.fao.org/3/J6667E/J6667E.pdf.

  • Independent External Evaluation of FAO’s Role and Work in Statistics (Food and Agriculture Organization of the United Nations, 2008); https://www.fao.org/3/bd418e/bd418e.pdf.

  • Iizumi, T. et al. Historical changes in global yields: major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357 (2014).

    Article 

    Google Scholar 

  • Gangopadhyay, P. K., Shirsath, P. B., Dadhwal, V. K. & Aggarwal, P. K. A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India. Sci. Data 9, 730 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wilkinson, M. D. et al. Comment: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Leff, B., Ramankutty, N. & Foley, J. A. Geographic distribution of major crops across the world. Glob. Biogeochem. Cycles 18, GB1009 (2004).

    Article 
    ADS 

    Google Scholar 

  • Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

    Article 
    ADS 

    Google Scholar 

  • Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22, GB1003 (2008).

    Article 
    ADS 

    Google Scholar 

  • Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Lombardozzi, D. L., Bonan, G. B., Levis, S. & Lawrence, D. M. Changes in wood biomass and crop yields in response to projected CO2, O3, nitrogen deposition, and climate. J. Geophys. Res. Biogeosci. 123, 3262–3282 (2018).

    Article 

    Google Scholar 

  • Rolle, M., Tamea, S. & Claps, P. Improved large-scale crop water requirement estimation through new high-resolution reanalysis dataset. In EGU General Assembly (2020).

  • Fischer, G. et al. Global Agro-Ecological Zones (GAEZ v3.0) (FAO/IIASA, 2012).

  • Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).

    Article 

    Google Scholar 

  • Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Klein Goldewijk, K., Beusen, A., van Drecht, G. & de Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).

    Article 

    Google Scholar 

  • Kerner, H. et al. How accurate are existing land cover maps for agriculture in sub-Saharan Africa? Preprint at https://doi.org/10.48550/arXiv.2307.02575 (2023).

  • Meisner, J. et al. A time-series approach to mapping livestock density using household survey data. Sci. Rep. 12, 13310 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gilbert, M. et al. Global Cattle Distribution in 2015 (5 Minutes of Arc) (Harvard Dataverse, accessed 11 July 2023); https://doi.org/10.7910/DVN/LHBICE.

  • Da Re, D. et al. Downscaling livestock census data using multivariate predictive models: sensitivity to modifiable areal unit problem. PLoS One 15, e0221070 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nicolas, G. et al. Using random forest to improve the downscaling of global livestock census data. PLoS One 11, e0150424 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • MacLeod, M. et al. Greenhouse Gas Emissions from Pig and Chicken Supply Chains: a Global Life Cycle Assessment (FAO, 2013).

  • Opio, C. et al. Greenhouse Gas Emission from Ruminant Supply Chains (FAO, 2013).

  • Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Robinson, T. P. et al. Global Livestock Production Systems (FAO and ILRI, 2011).

  • Kruska, R. L., Reid, R. S., Thornton, P. K., Henninger, N. & Kristjanson, P. M. Mapping livestock-oriented agricultural production systems for the developing world. Agric. Syst. 77, 39–63 (2003).

    Article 

    Google Scholar 

  • Seré Rabé, C. & Steinfeld, H. World Livestock Production Systems: Current Status, Issues and Trends (FAO, 1996).

  • Dixon, J. A., Gibbon, D. P. & Gulliver, A. Farming Systems and Poverty: Improving Farmers’ Livelihoods in a Changing World (FAO, 2001).

  • Hammond, J. et al. The Rural Household Multi-Indicator Survey (RHoMIS) for rapid characterisation of households to inform climate smart agriculture interventions: description and applications in East Africa and Central America. Agric. Syst. 151, 225–233 (2017).

    Article 

    Google Scholar 

  • Zane, G. & Pica-Ciamarra, U. The contribution of livestock to household livelihoods in Tanzania and Uganda: measuring tradable and non-tradable livestock outputs. Trop. Anim. Health Prod. 53, 304 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Carletto, C. Better data, higher impact: improving agricultural data systems for societal change. Eur. Rev. Agric. Econ. 48, 719–740 (2021).

    Article 

    Google Scholar 

  • Carletto, C., Dillon, A. & Zezza, A. in Handbook of Agricultural Economics Vol. 5 (eds Barrett, C. B. & Just, D. R.) 4407–4480 (Elsevier, 2021).

  • Duncan, A. J., Lukuyu, B., Mutoni, G., Lema, Z. & Fraval, S. Supporting participatory livestock feed improvement using the Feed Assessment Tool (FEAST). Agron. Sustain. Dev. 43, 34 (2023).

    Article 

    Google Scholar 

  • Fritz, S. et al. A global dataset of crowdsourced land cover and land use reference data. Sci. Data 4, 170075 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • The State of World Fisheries and Aquaculture (SOFIA) (Food and Agriculture Organization of the United Nations, 2022); https://www.fao.org/3/cc0461en/online/sofia/2022/world-fisheries-aquaculture.html.

  • Food and Agriculture Organization of the United Nations. Coordinating Working Party on Fishery Statistics (CWP) Handbook (FAO, 2020).

  • Fishery and Aquaculture Statistics. Global Production by Production Source 1950–2020 (FishStatJ) (Food and Agricultural Organization of the United Nations, 2022); https://www.fao.org/fishery/en/topic/166235?lang=en.

  • Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data 4, 170039 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).

    Article 

    Google Scholar 

  • Pauly, D., Zeller, D. & Palomares, M.L.D. (eds) Sea Around Us Concepts, Design and Data (Sea Around Us, 2020); seaaroundus.org.

  • Pauly, D. & Zeller, D. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nat. Commun. 7, 10244 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fluet-Chouinard, E., Funge-Smith, S. & McIntyre, P. B. Global hidden harvest of freshwater fish revealed by household surveys. Proc. Natl Acad. Sci. 115, 7623–7628 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ye, Y. et al. FAO’s statistic data and sustainability of fisheries and aquaculture: comments on Pauly and Zeller (2017). Mar. Policy 81, 401–405 (2017).

    Article 

    Google Scholar 

  • Klinger, D. H. et al. Moving beyond the fished or farmed dichotomy. Mar. Policy 38, 369–374 (2013).

    Article 

    Google Scholar 

  • Froehlich, H. E. et al. Piecing together the data of the US marine aquaculture puzzle. J. Environ. Manag. 308, 114623 (2022).

    Article 

    Google Scholar 

  • Clawson, G. et al. Mapping the spatial distribution of global mariculture production. Aquaculture 553, 738066 (2022).

    Article 

    Google Scholar 

  • Ottinger, M., Bachofer, F., Huth, J. & Kuenzer, C. Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series. Remote Sens. 14, 153 (2021).

    Article 
    ADS 

    Google Scholar 

  • Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).

    Article 
    ADS 

    Google Scholar 

  • Stehman, S. V. & Foody, G. M. Key issues in rigorous accuracy assessment of land cover products. Remote Sens. Environ. 231, 111199 (2019).

    Article 

    Google Scholar 

  • Laso Bayas, J. C. et al. A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform. Sci. Data 4, 170136 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gourlay, S., Kilic, T. & Lobell, D. B. A new spin on an old debate: errors in farmer-reported production and their implications for inverse scale-productivity relationship in Uganda. J. Dev. Econ. 141, 102376 (2019).

    Article 

    Google Scholar 

  • Phelps, L. N. & Kaplan, J. O. Land use for animal production in global change studies: defining and characterizing a framework. Glob. Chang. Biol. 23, 4457–4471 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lowder, S. K., Sánchez, M. V. & Bertini, R. Which farms feed the world and has farmland become more concentrated? World Dev. 142, 105455 (2021).

    Article 

    Google Scholar 

  • van Andel, M., Tildesley, M. J. & Gates, M. C. Challenges and opportunities for using national animal datasets to support foot‐and‐mouth disease control. Transbound. Emerg. Dis. 68, 1800–1813 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Abebe, R. et al. Narratives and counternarratives on data sharing in Africa. In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 329–341 (2021).

  • Bradley, D. et al. Opportunities to improve fisheries management through innovative technology and advanced data systems. Fish Fish. 20, 564–583 (2019).

    Article 

    Google Scholar 

  • van Helmond, A. T. M. et al. Electronic monitoring in fisheries: lessons from global experiences and future opportunities. Fish Fish. 21, 162–189 (2020).

    Article 

    Google Scholar 

  • Seto, K. L. et al. Fishing through the cracks: the unregulated nature of global squid fisheries. Sci. Adv. 9, eadd8125 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Taconet, M. et al. Global Atlas of AIS-Based Fishing Activity: Challenges and Opportunities (FAO, 2019).

  • Welch, H. et al. Hot spots of unseen fishing vessels. Sci. Adv. 8, eabq2109 (2023).

    Article 
    ADS 

    Google Scholar 

  • Orofino, S., McDonald, G., Mayorga, J., Costello, C. & Bradley, D. Opportunities and challenges for improving fisheries management through greater transparency in vessel tracking. ICES J. Mar. Sci. 80, 675–689 (2023).

    Article 

    Google Scholar 

  • Shepperson, J. L. et al. A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints. ICES J. Mar. Sci. 75, 988–998 (2018).

    Article 

    Google Scholar 

  • Kroodsma, D. A. et al. Revealing the Global Longline Fleet with Satellite Radar (2022).

  • Park, J. et al. Illuminating dark fishing fleets in North Korea. Sci. Adv. 6, eabb1197 (2023).

    Article 
    ADS 

    Google Scholar 

  • Ottinger, M., Clauss, K. & Kuenzer, C. Large-scale assessment of coastal aquaculture ponds with Sentinel-1 time series data. Remote Sens. 9, 440 (2017).

    Article 
    ADS 

    Google Scholar 

  • Piñeiro, V. et al. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat. Sustain. 3, 809–820 (2020).

    Article 

    Google Scholar 

  • Rotz, S. et al. The politics of digital agricultural technologies: a preliminary review. Sociol. Ruralis 59, 203–229 (2019).

    Article 

    Google Scholar 

  • Xu, Y. et al. Mapping aquaculture areas with multi-source spectral and texture features: a case study in the Pearl River basin (Guangdong), China. Remote Sens. 13, 4320 (2021).

    Article 
    ADS 

    Google Scholar 

  • Cochrane, K. (ed.) Illuminating Hidden Harvests: the Contributions of Small-Scale Fisheries to Sustainable Development (FAO, Duke Univ. & World Fish, 2023).

  • Halim, A. et al. Developing a functional definition of small-scale fisheries in support of marine capture fisheries management in Indonesia. Marine Policy 100, 238–248 (2018).

    Article 

    Google Scholar 

  • Smith, H. & Basurto, X. Defining small-scale fisheries and examining the role of science in shaping perceptions of who and what counts: a systematic review. Front. Mar. Sci. 6, 236 (2019).

    Article 

    Google Scholar 

  • Carletto, C., Jolliffe, D. & Banerjee, R. From tragedy to renaissance: improving agricultural data for better policies. J. Dev. Stud. 51, 133–148 (2015).

    Article 

    Google Scholar 

  • Agarwal, S., Singh, V. & Gandhi, R. Could a data sharing protocol be agriculture’s missing link? The Chicago Council on Global Affairs https://globalaffairs.org/commentary-and-analysis/blogs/could-data-sharing-protocol-be-agricultures-missing-link (2021).

  • Fisher, A. & Fukuda-Parr, S. Introduction — data, knowledge, politics and localizing the SDGs. J. Hum. Dev. Capab. 20, 375–385 (2019).

    Article 

    Google Scholar 

  • Montenegro de Wit, M. & Canfield, M. Feeding the world, byte by byte’: emergent imaginaries of data productivism. J. of Peasant Stud. https://doi.org/10.1080/03066150.2023.2232997 (2023).

  • Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M.-J. Big data in smart farming — a review. Agric. Syst. 153, 69–80 (2017).

    Article 

    Google Scholar 

  • Spanaki, K., Karafili, E. & Despoudi, S. AI applications of data sharing in agriculture 4.0: a framework for role-based data access control. Int. J. Inf. Manag. 59, 102350 (2021).

    Article 

    Google Scholar 

  • Brinkerhoff, D. W. & Brinkerhoff, J. M. Public–private partnerships: perspectives on purposes, publicness, and good governance. Public Adm. Dev. 31, 2–14 (2011).

    Article 

    Google Scholar 

  • Wiggins, S., Kirsten, J. & Llambí, L. The future of small farms. World Dev. 38, 1341–1348 (2010).

    Article 

    Google Scholar 

  • Jouanjean, M.-A., Casalini, F., Wiseman, L. & Gray, E. Issues Around Data Governance in the Digital Transformation of Agriculture: The Farmers’ Perspective (OECD, 2020).

  • Jensen, Ø., Dempster, T., Thorstad, E. B., Uglem, I. & Fredheim, A. Escapes of fishes from Norwegian sea-cage aquaculture: causes, consequences and prevention. Aquacult. Environ. Interact. 1, 71–83 (2010).

  • Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Herrero, M. et al. Innovation can accelerate the transition towards a sustainable food system. Nat. Food 1, 266–272 (2020).

    Article 

    Google Scholar 

  • Barrett, C. B. et al. Bundling innovations to transform agri-food systems. Nat. Sustain. 3, 974–976 (2020).

    Article 

    Google Scholar 

  • Paliyam, M., Nakalembe, C., Liu, K., Nyiawung, R. & Kerner, H. Street2sat: a machine learning pipeline for generating ground-truth geo-referenced labeled datasets from street-level images. In ICML 2021 Workshop on Tackling Climate Change with Machine Learning (ICML, 2021).

  • Yan, Y. & Ryu, Y. Exploring Google Street View with deep learning for crop type mapping. ISPRS J. Photogramm. Remote Sens. 171, 278–296 (2021).

    Article 
    ADS 

    Google Scholar 

  • d’Andrimont, R., Yordanov, M., Martinez-Sanchez, L. & Van der Velde, M. Monitoring crop phenology with street-level imagery using computer vision. Comput. Electron. Agric. 196, 106866 (2022).

    Article 

    Google Scholar 

  • van der Merwe, D., Burchfield, D. R., Witt, T. D., Price, K. P. & Sharda, A. Drones in agriculture. Adv. Agron. 162, 1–30 (2020).

    Article 

    Google Scholar 

  • d’Andrimont, R. et al. Crowdsourced street-level imagery as a potential source of in-situ data for crop monitoring. Land 7, 127 (2018).

    Article 

    Google Scholar 

  • Kerner, H. R. et al. Phenological normalization can improve in-season classification of maize and soybean: a case study in the central US Corn Belt. Sci. Remote Sens. 6, 100059 (2022).

    Article 

    Google Scholar 

  • Wang, S. et al. Mapping crop types in southeast India with smartphone crowdsourcing and deep learning. Remote Sens. 12, 2957 (2020).

    Article 
    ADS 

    Google Scholar 

  • Tseng, G., Kerner, H., Nakalembe, C. & Becker-Reshef, I. Learning to predict crop type from heterogeneous sparse labels using meta-learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 1111–1120 (2021).

  • Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H. & Islam, N. A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sens. 14, 1990 (2022).

    Article 
    ADS 

    Google Scholar 

  • Deines, J. M., Wang, S. & Lobell, D. B. Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt. Environ. Res. Lett. 14, 124038 (2019).

    Article 
    ADS 

    Google Scholar 

  • Ferrag, M. A., Shu, L., Yang, X., Derhab, A. & Maglaras, L. Security and privacy for green IoT-based agriculture: review, blockchain solutions, and challenges. IEEE Access 8, 32031–32053 (2020).

    Article 

    Google Scholar 

  • Rahman, M. U., Baiardi, F. & Ricci, L. Blockchain smart contract for scalable data sharing in IoT: a case study of smart agriculture. In 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) 1–7 (IEEE, 2020).

  • Gobezie, T. B. & Biswas, A. Break barriers in soil data stewardship by rewarding data generators. Nat. Rev. Earth Environ. 4, 353–354 (2023).

    Article 
    ADS 

    Google Scholar 

  • Durrant, A. et al. The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Comput. Electron. Agric. 193, 106648 (2022).

    Article 

    Google Scholar 

  • UNSC. Spatial anonymization: guidance note for the Inter-Secretariat Working Group on Household Surveys. https://unstats.un.org/unsd/statcom/52nd-session/documents/BG-3l-Spatial_Anonymization-E.pdf (2021).

  • Tedeschi, L. O. et al. Quantification of methane emitted by ruminants: a review of methods. J. Anim. Sci. 100, skac197 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ramayo-Caldas, Y. et al. Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows. J. Anim. Breed. Genet. 137, 49–59 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Han, C. S. et al. Invited review: Sensor technologies for real-time monitoring of the rumen environment. J. Dairy Sci. 105, 6379–6404 (2022).

  • Tullo, E., Finzi, A. & Guarino, M. Review: Environmental impact of livestock farming and precision livestock farming as a mitigation strategy. Sci. Total Environ. 650, 2751–2760 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Chase, L. E. & Fortina, R. Environmental and economic responses to precision feed management in dairy cattle diets. Agriculture https://doi.org/10.3390/agriculture13051032 (2023).

  • Mackenzie, S. in Smart Livestock Nutrition 311–336 (Springer, 2023).

  • Sala, E. et al. The economics of fishing the high seas. Sci. Adv. 4, eaat2504 (2023).

    Article 
    ADS 

    Google Scholar 

  • White, T. D. et al. Predicted hotspots of overlap between highly migratory fishes and industrial fishing fleets in the northeast Pacific. Sci. Adv. 5, eaau3761 (2023).

    Article 
    ADS 

    Google Scholar 

  • Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572, 461–466 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • White, T. D. et al. Assessing the effectiveness of a large marine protected area for reef shark conservation. Biol. Conserv. 207, 64–71 (2017).

    Article 

    Google Scholar 

  • McDermott, G. R., Meng, K. C., McDonald, G. G. & Costello, C. J. The blue paradox: preemptive overfishing in marine reserves. Proc. Natl Acad. Sci. 116, 5319–5325 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Cabral, R. B. et al. Rapid and lasting gains from solving illegal fishing. Nat. Ecol. Evol. 2, 650–658 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Behivoke, F. et al. Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests. Ecol. Indic. 123, 107321 (2021).

    Article 

    Google Scholar 

  • Tilley, A., Dos Reis Lopes, J. & Wilkinson, S. P. PeskAAS: a near-real-time, open-source monitoring and analytics system for small-scale fisheries. PLoS One 15, e0234760 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Snapir, B., Waine, T. W. & Biermann, L. Maritime vessel classification to monitor fisheries with SAR: demonstration in the North Sea. Remote Sens. 11, 353 (2019).

    Article 
    ADS 

    Google Scholar 

  • Sarda, K., CaJacob, D., Orr, N. & Zee, R. Making the invisible visible: precision RF-emitter geolocation from space by the Hawkeye 360 Pathfinder mission. In 32nd Annual AIAA/USU Conference on Small Satellites (AIAA, USU, 2018).

  • Iacarella, J. C. et al. Application of AIS- and flyover-based methods to monitor illegal and legal fishing in Canada’s Pacific marine conservation areas. Conserv. Sci. Pract. 5, e12926 (2023).

    Article 

    Google Scholar 

  • Prayudi, A., Sulistijono, I. A., Risnumawan, A. & Darojah, Z. Surveillance system for illegal fishing prevention on UAV imagery using computer vision. In 2020 International Electronics Symposium (IES) 385–391 (2020).

  • Bartholomew, D. C. et al. Remote electronic monitoring as a potential alternative to on-board observers in small-scale fisheries. Biol. Conserv. 219, 35–45 (2018).

    Article 

    Google Scholar 

  • Antonucci, F. & Costa, C. Precision aquaculture: a short review on engineering innovations. Aquacult. Int. 28, 41–57 (2020).

    Article 

    Google Scholar 

  • Rastegari, H. et al. Internet of Things in aquaculture: a review of the challenges and potential solutions based on current and future trends. Smart Agric. Technol. 4, 100187 (2023).

    Article 

    Google Scholar 

  • Cervantes-Godoy, D. et al. The Future of Food and Agriculture: Trends and Challenges Vol. 4 (FAO, 2014).

  • Turnheim, B. et al. Evaluating sustainability transitions pathways: bridging analytical approaches to address governance challenges. Glob. Environ. Chang. 35, 239–253 (2015).

    Article 

    Google Scholar 

  • Dawes, S. S. Stewardship and usefulness: policy principles for information-based transparency. Gov. Inf. Q. 27, 377–383 (2010).

    Article 

    Google Scholar 

  • Xie, W. et al. Crop switching can enhance environmental sustainability and farmer incomes in China. Nature https://doi.org/10.1038/s41586-023-05799-x (2023).

  • Kochupillai, M., Kahl, M., Schmitt, M., Taubenböck, H. & Zhu, X. X. Earth observation and artificial intelligence: understanding emerging ethical issues and opportunities. IEEE Geosci. Remote Sens. Mag. 10, 90–124 (2022).

    Article 

    Google Scholar 

  • World Bank. World Development Report 2021: Data for Better Lives (World Bank, 2021).

  • Sachs, J. D. et al. Six transformations to achieve the sustainable development goals. Nat. Sustain. 2, 805–814 (2019).

  • Fanzo, J. et al. Viewpoint: Rigorous monitoring is necessary to guide food system transformation in the countdown to the 2030 global goals. Food Policy 104, 102163 (2021).

    Article 

    Google Scholar 

  • Cassidy, E. S., West, P. C., Gerber, J. S. & Foley, J. A. Redefining agricultural yields: from tonnes to people nourished per hectare. Environ. Res. Lett. 8, 034015 (2013).

    Article 
    ADS 

    Google Scholar 

  • Iizumi, T. & Sakai, T. The global dataset of historical yields for major crops 1981–2016. Sci. Data 7, 97 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Franke, J. A. et al. The GGCMI phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels. Geosci. Model Dev. 13, 2315–2336 (2020).

    Article 
    ADS 

    Google Scholar 

  • Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Müller, C. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci. Data 6, 50 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • The Global Yield Gap and Water Productivity Atlas (GYGA) (Yield Gap, 2022); http://www.yieldgap.org.

  • Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Jackson, N. D., Konar, M., Debaere, P. & Estes, L. Probabilistic global maps of crop-specific areas from 1961 to 2014. Environ. Res. Lett. 14, 094023 (2019).

    Article 
    ADS 

    Google Scholar 

  • Ray, D. K. et al. Climate change has likely already affected global food production. PLoS One 14, e0217148 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • International Food Policy Research Institute. Global spatially-disaggregated crop production statistics data for 2000 version 3.0.7. https://doi.org/10.7910/DVN/A50I2T (2019).

  • International Food Policy Research Institute (IFPRI), International Institute for Applied Systems Analysis (IIASA). Global Spatially-disaggregated crop production statistics data for 2005 version 3.2. https://doi.org/10.7910/DVN/DHXBJX (2016).

  • International Food Policy Research Institute. Global spatially-disaggregated crop production statistics data for 2010 version 2.0. https://doi.org/10.7910/DVN/PRFF8V (2019).

  • West, P. C. et al. Leverage points for improving global food security and the environment. Science 345, 325–328 (2014).

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