A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

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

Estimating the Global Distribution of Field Size using Crowdsourcing

There is 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, e.g. 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 130K 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 modelling, 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

Estimating the Global Distribution of Field Size using Crowdsourcing

There is 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, e.g. 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 130K 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 modelling, 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

Methodology for generating a global forest management layer

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects

Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with Sentinel-2

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of "Sentinel-2 like" time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the "Joint Experiment for Crop Assessment and Monitoring" network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system. © 2015 by the authors

Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with Sentinel-2

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of "Sentinel-2 like" time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the "Joint Experiment for Crop Assessment and Monitoring" network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system. © 2015 by the authors

Soil phosphorus fractions in sandy soils amended with cattle manure for long periods Frações de fósforo em solos arenosos adubados com esterco por longos períodos

Phosphorus fractions were determined in soil samples from areas fertilized or not with farmyard cattle manure (FYM) and in samples of FYM used in the semi-arid region of Paraiba state, Brazil. Soil samples were taken from the 0-20; 20-40 and 40-60 cm layers of 18 cultivated areas, which, according to interviews with farmers, had been treated with 12 to 20 t ha-1 FYM annually, for the past 2 to 40 years. Soil samples were also collected from four unfertilized pasture areas as controls. Phosphorus in the soil samples was sequentially extracted with water (Pw), resin (Pres), NaHCO3 (Pi bic and Po bic), NaOH (Pi hid and Po hid), H2SO4 (Pacid) and, finally, by digestion with H2SO4/H2O2 (Presd). Nine FYM samples were extracted with water, resin, Mehlich-1, H2SO4, NaOH or digestion with H2SO4/H2O2, not sequentially, and the extracts analyzed for P. The sampled areas had homogeneous, sandy and P-deficient soils; increases in total soil P (Pt) above the mean value of the control areas (up to 274 mg kg-1 in the 0-20 cm layer of the most P-enriched samples) were therefore attributed to FYM applications, which was the only external P input in the region. Regression analysis was used to study the relationship between soil P fractions and Pt. The Pacid fraction, related to Ca-P forms, showed the greatest increases (p < 0.01) as a result of FYM applications, rising from 8.4 mg kg-1 in a non-fertilized sample to 43.8 mg kg-1 in the sample with the highest Pt content. The sum of Pw, Pres and Pi bic, considered as labile P, showed comparable increases with Pacid, while Pi hid showed the smallest increase due to FYM applications. Organic P forms also increased, more so the fraction Po hid, considered less labile, than the more labile one, Po bic. The residual P fraction was practically half of Pt, independently of the Pt value. Increases in labile P, Pacid and organic P were justified by the high average concentration of Pw (36 %), Pacid (34 %), and Po hid (30 %) in the FYM. Significant changes in the proportion of P forms among soil layers indicated the downward movement of P in organic forms.<br>Frações de P foram quantificadas em amostras de solo obtidas em áreas não adubadas e adubadas com esterco bovino e em amostras do esterco utilizado na região agreste do estado da Paraíba, Brasil. As amostras de solo foram coletadas nas camadas de 0-20, 20-40 e 40- 60 cm em 18 áreas agrícolas que, pelos históricos levantados junto aos agricultores, vinham recebendo entre 12 e 20 Mg ha-1 de esterco anualmente, por períodos variando entre 2 e 40 anos. Como controle, foram retiradas amostras de solo em quatro áreas sob pastagem sem histórico de adubação. O P nas amostras de solo foi sequencialmente extraído com água (Pw), resina (Pres), NaHCO3 (Pi bic e Po bic), NaOH (Pi hid e Po hid), H2SO4 (Pácido) e, finalmente, por digestão com H2SO4/H2O2 (Presd). Em nove amostras de esterco foi determinado o P extraível por água, resina, Mehlich-1, H2SO4, NaOH e digestão com H2SO4/H2O2, não sequencialmente. As áreas amostradas apresentam solos homogêneos, arenosos e muito deficientes em P, de forma que a variação encontrada no teor de P total (Pt), entre um mínimo de 50 mg kg-1 em área controle e um máximo de 393 mg kg-1 em área adubada, foi atribuída às adições de esterco, único insumo externo de P na região. As concentrações de P do solo nas frações foram analisadas por regressão em relação aos teores de P total (Pt). A fração Pácido, considerada como P ligado ao Ca, foi a que apresentou o maior aumento (p < 0,01) como resultado das adições de esterco, passando de 8,4 mg kg-1 em amostra não adubada para 43,8 mg kg-1 na amostra com maior teor de Pt. A soma de Pw, Pres e Pi bic, considerada como P lábil, apresentou acréscimos semelhantes à fração Pácido, enquanto que, a fração Pi hid, mostrou o menor aumento em função da adição de esterco. As formas de P orgânico também aumentaram, sendo maiores (p < 0,01) os aumentos da fração Po hid, menos lábil, que os da fração Po bic. O fósforo residual foi praticamente metade do Pt, em toda a faixa de variação deste último. Os acréscimos no P lábil, Pácido e P orgânico foram justificados pelo elevado teor médio de P solúvel em água (36 %), de P ligado ao Ca (34 %) e de P em formas orgânicas (30 %) do esterco. Variações significativas nas proporções de P entre as camadas de solo indicaram que, nas áreas adubadas, houve movimento descendente do P em formas orgânicas