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dc.contributorBiradar, Chandrashekharen_US
dc.contributorAtassi, Layalen_US
dc.contributorMoussadek, Rachiden_US
dc.contributorKharrat, Mohameden_US
dc.contributorSingh, Murarien_US
dc.contributorAndaloussi, Fouad Abbaden_US
dc.contributorAgrawal, Shiv Kumaren_US
dc.creatorPatil, Prashanten_US
dc.identifier.citationPrashant Patil, Chandrashekhar Biradar, Layal Atassi, Rachid Moussadek, Mohamed Kharrat, Murari Singh, Fouad Abbad Andaloussi, Shiv Kumar Agrawal. (20/7/2015). Mapping and monitoring of food legumes and dryland cereal production systems. Istanbul, Turkey.en_US
dc.description.abstractMapping and monitoring of the agricultural production systems on a regular interval provide important spatial matrix on the status, trend, and options for effective intervention at multiple scales. The recent advances in agrogeoinformatics bigdata enriched with increasing openaccess protocols become an integral part of solving the food security equation. This paper demonstrates use of an integrated earth observation system (EOS) for mapping and monitoring major agricultural production systems. The approach uses multitemporal and multiscale remote sensing data coupled with insitu observation to map the legume and cereal production systems. The support vector machine (SVM) classification was found to be the best with overall classification accuracy of 82%. The insitu data on crop grain and straw yields were measured using nested sampling approach. The best fit equation of yield values were regressed with remote sensing indices (NDVI and EVI). The significant correlation (R ) value of cereal and lentil crop were 0.74 and 6.9 at p<;0.01 respectively. The R value between observed yield and predicted yield was 0.80 and 0.97 in cereal and lentil crops respectively. The predicted yield based on remote sensing data varies from 3,303 to 5,710 kg ha and mean yield is 3,840 kg ha . The productivity of the cereal crop was varies from 4228 kg ha to 4598 kg ha while lentil crop was between 304 to 1,500 kg ha . The huge inter and intra field variably was observed through the study areas. Such information yielded vital information about yield gaps exists within and across the fields. Study is in progress to develop systematic and semiautomated algorithms to map and monitor the agricultural production on regular interval to quantify the changes in the cropping pattern, rotation, production and impacts of the technological interventions and exante analysisen_US
dc.sourceFourth International Conference on Agro-Geoinformatics IEEE Explore, Agro-Geoinformatics 2015;en_US
dc.titleMapping and monitoring of food legumes and dryland cereal production systemsen_US
dc.typeConference Paperen_US
cg.creator.idBiradar, Chandrashekhar: 0000-0002-9532-9452en_US
cg.creator.idAtassi, Layal: 0000-0002-7271-7591en_US
cg.creator.idSingh, Murari: 0000-0001-5450-0949en_US
cg.creator.idAgrawal, Shiv Kumar: 0000-0001-8407-3562en_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerNational Institute of Agronomic Research Morocco - INRA Moroccoen_US
cg.contributor.centerNational Agricultural Research Institute of Tunisia - INRATen_US
cg.contributor.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderInternational Fund for Agricultural Development - IFADen_US
cg.contributor.projectEnhanced small-holder wheat-legume cropping systems to improve food security under changing climate in the drylands of West Asia and North Africaen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionNorthern Africaen_US
dc.identifier.statusTimeless limited accessen_US

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