dc.contributor | Biradar, Chandrashekhar | en_US |
dc.contributor | Atassi, Layal | en_US |
dc.contributor | Moussadek, Rachid | en_US |
dc.contributor | Kharrat, Mohamed | en_US |
dc.contributor | Singh, Murari | en_US |
dc.contributor | Andaloussi, Fouad Abbad | en_US |
dc.contributor | Agrawal, Shiv Kumar | en_US |
dc.creator | Patil, Prashant | en_US |
dc.date | 2015-07-20 | en_US |
dc.date.accessioned | 2016-09-20T11:16:32Z | |
dc.date.available | 2016-09-20T11:16:32Z | |
dc.identifier | https://mel.cgiar.org/dspace/limited | en_US |
dc.identifier | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7248158 | en_US |
dc.identifier.citation | Prashant 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.identifier.uri | https://hdl.handle.net/20.500.11766/4930 | |
dc.description.abstract | Mapping 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
analysis | en_US |
dc.format | PDF | en_US |
dc.language | en | en_US |
dc.publisher | IEEEXPLORE | en_US |
dc.rights | CC-BY-NC-4.0 | en_US |
dc.source | Fourth International Conference on Agro-Geoinformatics IEEE Explore, Agro-Geoinformatics 2015; | en_US |
dc.title | Mapping and monitoring of food legumes and dryland cereal production systems | en_US |
dc.type | Conference Paper | en_US |
cg.creator.id | Biradar, Chandrashekhar: 0000-0002-9532-9452 | en_US |
cg.creator.id | Atassi, Layal: 0000-0002-7271-7591 | en_US |
cg.creator.id | Singh, Murari: 0000-0001-5450-0949 | en_US |
cg.creator.id | Agrawal, Shiv Kumar: 0000-0001-8407-3562 | en_US |
cg.creator.ID-type | ORCID | en_US |
cg.creator.ID-type | ORCID | en_US |
cg.creator.ID-type | ORCID | en_US |
cg.creator.ID-type | ORCID | en_US |
cg.subject.agrovoc | legumes | en_US |
cg.subject.agrovoc | monitoring | en_US |
cg.contributor.center | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.contributor.center | National Institute of Agronomic Research Morocco - INRA Morocco | en_US |
cg.contributor.center | National Agricultural Research Institute of Tunisia - INRAT | en_US |
cg.contributor.crp | CGIAR Research Program on Dryland Systems - DS | en_US |
cg.contributor.funder | International Fund for Agricultural Development - IFAD | en_US |
cg.contributor.project | Enhanced small-holder wheat-legume cropping systems to improve food security under changing climate in the drylands of West Asia and North Africa | en_US |
cg.contributor.project-lead-institute | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.date.embargo-end-date | Timeless | en_US |
cg.coverage.region | Northern Africa | en_US |
cg.coverage.country | MA | en_US |
cg.contact | patil370.iirs@gmail.com | en_US |
dc.identifier.status | Timeless limited access | en_US |
mel.project.open | https://mel.cgiar.org/projects/46 | en_US |