There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
Duck enteritis virus (DEV) causes an acute and contagious infection in duck. The present study was carried out to evaluate the pathogenicity and pathodynamics of DEV isolates from different natural ...outbreaks in the Assam Province of India. A total of six wild-type isolates of DEV were revived in ducklings to determine its biologic characterization. Postmortem examination of infected ducklings revealed DEV-specific gross lesions in different organs. The presence of DEV was confirmed by its genome amplification and the presence of viral antigens from collected tissue samples by indirect fluorescent antibody test. All the isolates revived in ducklings were further propagated in duck embryo fibroblast cells. Highly virulent and low virulent isolates of DEV were selected for further study based on median duck infectivity dose (DID50) and median tissue culture infectivity dose (TCID50). The highly virulent isolate of DEV had values of 102 DID50/ml and 106.33 TCID50/ml, whereas the low virulent strain had titers of 10 DID50/ml and 104.83 TCID50/ml in the cell culture. Our results showed replication of DEV in ducks with the highest and lowest viral titers in the thymus and bursa of Fabricius, respectively. In addition, microscopic analysis revealed necrosis and degeneration of submucosal esophageal glands and glandular epithelium. The study will be useful to understand the organ tropism and pathologic alteration among the virulent DEV isolates.
A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around ...the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.
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