{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:54:50Z","timestamp":1760151290096,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000200","name":"United States Agency for International Development","doi-asserted-by":"publisher","award":["AID-FFP-A-14-00008"],"award-info":[{"award-number":["AID-FFP-A-14-00008"]}],"id":[{"id":"10.13039\/100000200","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ren Che Foundation","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 2020\u20132021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet we are unaware of peer-reviewed studies that quantitatively link variations in weather and climate to agricultural outcomes for staple crops in Madagascar. In this study, we use historical data to empirically assess the relationship between soil moisture and food production. Specifically, we focus on major staple crops that form the foundation of Malagasy food systems and nutrition, including rice, which accounts for 46% of the average Malagasy caloric intake, as well as cassava, maize, and sweet potato. Available data associated with survey-based crop statistics constrain our analysis to 2010\u20132017 across four clusters of Malagasy districts. Strong correlations are observed between remotely sensed soil moisture and rice production, ranging between 0.67 to 0.95 depending on the cluster and choice of crop calendar. Predictions are shown to be statistically significant at the 90% confidence level using bootstrapping techniques, as well as through an out-of-sample prediction framework. Soil moisture also shows skill in predicting cassava, maize, and sweet potato production, but only when the months most vulnerable to water stress are isolated. Additional analyses using more survey data, as well as potentially more-refined crop maps and calendars, will be useful for validating and improving soil-moisture-based predictions of yield.<\/jats:p>","DOI":"10.3390\/rs14051223","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T22:53:25Z","timestamp":1646261605000},"page":"1223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Retrospective Predictions of Rice and Other Crop Production in Madagascar Using Soil Moisture and an NDVI-Based Calendar from 2010\u20132017"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-6602","authenticated-orcid":false,"given":"Angela J.","family":"Rigden","sequence":"first","affiliation":[{"name":"Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02134, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-7493","authenticated-orcid":false,"given":"Christopher","family":"Golden","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Harvard T.H. Chan School of Public Heath, Boston, MA 02115, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-8145","authenticated-orcid":false,"given":"Peter","family":"Huybers","sequence":"additional","affiliation":[{"name":"Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02134, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","unstructured":"INSTAT, and UNICEF (2019). Multiple Indicator Cluster Survey\u2013MICS Madagascar, 2018, Final Report, INSTAT and UNICEF. Technical Report."},{"key":"ref_2","unstructured":"FAO (2019). Madagascar\u2013Impact of Early Warning Early Action, FAO. Technical Report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20130089","DOI":"10.1098\/rstb.2013.0089","article-title":"Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar","volume":"369","author":"Harvey","year":"2014","journal-title":"Philos. Trans. R. Soc. Biol. 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