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

Vegetation traits of pre-Alpine grasslands in southern Germany

The data set contains information on aboveground vegetation traits of > 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project (“Sustainable use of alpine and pre-alpine grassland soils in a changing climate”; https://www.susalps.de/) to provide in-situ data for the calibration and validation of remote sensing based models to estimate grassland traits

Conditional Political Budget Cycles in Argentine Provinces

This paper presents evidence of electoraly-motivated changes in the budget balance, public expenditures, composition of public expenditures and provincial revenues in Argentine provinces. The empirical study is made using panel data analysis for 22 provinces during the period 1985-2001. Unconditional results show that conditioning on the alignment of provincial and federal executives (same political party in power) there is evidence of systematic changes in fiscal policies around elections. The observed changes support the predictions of rational opportunistic models of PBC. In election years, total provincial expenditures increase in aligned provinces, without affecting the fiscal balance, because to the increased discretional transfers from the federal government supporting the provincial incumbent federal revenues. By contrast, deficit increases for unaligned provinces. In addition, expenditure shifts toward current spending and away from capital spending for unaligned provinces in electoral years

Grassland yield estimations – potentials and limitations of remote sensing in comparison to process-based modeling and field measurements

Grasslands make up the majority of agricultural land and provide fodder for livestock. Information on grassland yield is very limited, as fodder is directly used at farms. However, data on grassland yields would be needed to inform politics and stakeholders on grassland ecosystem services and interannual variations. Grassland yield patterns often vary on small scales in Germany, and estimations are further complicated by missing information on grassland management. Here, we compare three different approaches to estimate annual grassland yield for a study region in southern Germany. We apply (i) a novel approach based on a model derived from field samples, satellite data and mowing information (RS); (ii) the biogeochemical process-based model LandscapeDNDC (LDNDC); and (iii) a rule set approach based on field measurements and spatial information on grassland productivity (RVA) to derive grassland yields per parcel for the Ammer catchment area in 2019. All three approaches reach plausible results of annual yields of around 4–9 t ha−1 and show overlapping as well as diverging spatial patterns. For example, direct comparisons show that higher yields were derived with LDNDC compared to RS and RVA, in particular related to the first cut and for grasslands mown only one or two times per year. The mowing frequency was found to be the most important influencing factor for grassland yields of all three approaches. There were no significant differences found in the effect of abiotic influencing factors, such as climate or elevation, on grassland yields derived from the different approaches. The potentials and limitations of the three approaches are analyzed and discussed in depth, such as the level of detail of required input data or the capability of regional and interannual yield estimations. For the first time, three different approaches to estimate grassland yields were compared in depth, resulting in new insights into their potentials and limitations. Grassland productivity maps provide the basis for the long-term analyses of climate and management impacts and comprehensive studies of the functions of grassland ecosystems.</p

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

Biomass estimation to support pasture management in Niger

Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998–2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available