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dc.contributorMutanga, Onisimoen_US
dc.contributorBiradar, Chandrashekharen_US
dc.creatorChivasa, Walteren_US
dc.date2017-08-17en_US
dc.date.accessioned2018-03-12T02:31:31Z
dc.date.available2018-03-12T02:31:31Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifierhttps://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1365390en_US
dc.identifierhttps://www.researchgate.net/publication/319164956_Application_of_remote_sensing_in_estimating_maize_grain_yield_in_heterogeneous_African_agricultural_landscapes_a_reviewen_US
dc.identifierhttp://dx.doi.org/10.1080/01431161.2017.1365390en_US
dc.identifier.citationWalter Chivasa, Onisimo Mutanga, Chandrashekhar Biradar. (17/8/2017). Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes. International Journal of Remote Sensing, 38(23), pp. 6816-6845.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11766/8110
dc.description.abstractMaize (Zea mays L.) is the second most commonly grown crop worldwide and number one staple food in Africa where it accounts for more than 50% of the energy requirements. However, despite its widespread cultivation and the significance of maize information in Africa, maize crop maps and yield forecasts are hardly available. Yet, systematic area, spatial distribution, and maize yield estimates are important in understanding and addressing food security in Africa. Objective monitoring of maize yield statisics in a systematic way is possible with remotely sensed data. However, absence of maize yield forecasts using remote sensing in Africa has been attributed to the cost of acquiring satellite imagery and the heterogeneity of agricultural landscapes. The recent advances in sensors technology and availability of free high-resolution (spatial and temporal) multispectral satellite images afford an opportunity to forecast maize yield as well as mapping its spatial distribution in near real-time basis. This review gives an overview of maize yield estimation using remotely sensed information and its potential application in a fragmented and highly granular agricultural landscapes in Africa, including inherent challenges and research needs. The review was motivated by challenges faced by researchers and national agricultural statistical services agents when forecasting maize yield using conventional ground-based survey methods. These problems include, but are not limited to, restricted accuracy, and cost and time spent resulting in missed opportunities in food security early warning systems and proper developmental interventions. We conclude that by picking multispectral sensors with high spatial, temporal, and spectral resolution, as well as appropriate classification techniques and accurate ground-truthing data, remote sensing can be a practical option for estimating maize grain yield and its spatio-temporal dynamics in heterogeneous African agricultural landscapes for designing appropriate developmental interventions and technological out scaling.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherTaylor & Francis: STM, Behavioural Science and Public Health Titlesen_US
dc.sourceInternational Journal of Remote Sensing;38,(2017) Pagination 6816,6845en_US
dc.subjectMaizeen_US
dc.titleApplication of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapesen_US
dc.typeJournal Articleen_US
cg.creator.ID0000-0002-9532-9452en_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocremote sensingen_US
cg.subject.agrovocafricaen_US
cg.contributor.centerThe African Seed Companyen_US
cg.contributor.centerUniversity of KwaZulu-Natal - UKZNen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.crpCRP on Dryland Systems - DSen_US
cg.contributor.crpCGIAR Strategic Results Framework Related - SRFen_US
cg.contributor.funderIndian Council of Agricultural Research - ICARen_US
cg.contributor.projectIndia Collaborative Program: Restricted funding for breeding for resistance to abiotic stresses in pulses & for 2017/2018 - 2021/2022en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.date.embargo-end-date2118-08-17en_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.countryINen_US
cg.contactwalter.chivasa@seedcogroup.comen_US
dc.identifier.statusLimited accessen_US
mel.project.openhttp://geoagro.icarda.org/india/en_US


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