17 research outputs found
Adapting Agriculture to Climate Change: A Synopsis of Coordinated National Crop Wild Relative Seed Collecting Programs across Five Continents
The Adapting Agriculture to Climate Change Project set out to improve the diversity,
quantity, and accessibility of germplasm collections of crop wild relatives (CWR). Between 2013 and
2018, partners in 25 countries, heirs to the globetrotting legacy of Nikolai Vavilov, undertook seed
collecting expeditions targeting CWR of 28 crops of global significance for agriculture. Here, we
describe the implementation of the 25 national collecting programs and present the key results. A total
of 4587 unique seed samples from at least 355 CWR taxa were collected, conserved ex situ, safety
duplicated in national and international genebanks, and made available through the Multilateral
System (MLS) of the International Treaty on Plant Genetic Resources for Food and Agriculture (Plant
Treaty). Collections of CWR were made for all 28 targeted crops. Potato and eggplant were the most
collected genepools, although the greatest number of primary genepool collections were made for
rice. Overall, alfalfa, Bambara groundnut, grass pea and wheat were the genepools for which targets
were best achieved. Several of the newly collected samples have already been used in pre-breeding
programs to adapt crops to future challenges.info:eu-repo/semantics/publishedVersio
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Effects of conversion of native cerrado vegetation to pasture on soil hydro-physical properties, evapotranspiration and streamflow on the Amazonian agricultural frontier
Understanding the impacts of land-use change on landscape-hydrological dynamics is one of the main challenges in the Northern Brazilian Cerrado biome, where the Amazon agricultural frontier is located. Motivated by the gap in literature assessing these impacts, we characterized the soil hydro-physical properties and quantified surface water fluxes from catchments under contrasting land-use in this region. We used data from field measurements in two headwater micro-catchments with similar physical characteristics and different land use, i.e. cerrado sensu stricto vegetation and pasture for extensive cattle ranching. We determined hydraulic and physical properties of the soils, applied ground-based remote sensing techniques to estimate evapotranspiration, and monitored streamflow from October 2012 to September 2014. Our results show significant differences in soil hydro-physical properties between the catchments, with greater bulk density and smaller total porosity in the pasture catchment. We found that evapotranspiration is smaller in the pasture (639 ± 31% mm yr-1) than in the cerrado catchment (1,004 ± 24% mm yr-1), and that streamflow from the pasture catchment is greater with runoff coefficients of 0.40 for the pasture and 0.27 for the cerrado catchment. Overall, our results confirm that conversion of cerrado vegetation to pasture causes soil hydro-physical properties deterioration, reduction in evapotranspiration reduction, and increased streamflow
Correction: Effects of conversion of native cerrado vegetation to pasture on soil hydro-physical properties, evapotranspiration and streamflow on the Amazonian agricultural frontier.
[This corrects the article DOI: 10.1371/journal.pone.0179414.]
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STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests
Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameter kB−1 and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides, λET caused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF
Overview of the Amazon and Cerrado biomes, the deforestation extension in the Legal Amazon, and the location of the cerrado and pasture catchments.
<p>Deforestation data from: IMAZON [Internet]; 2016. Available from: <a href="http://www.imazongeo.org.br/doc/downloads.php" target="_blank">http://www.imazongeo.org.br/doc/downloads.php</a>; and MMA [Internet]; 2016. Available from: <a href="http://mapas.mma.gov.br/i3geo/datadownload.htm" target="_blank">http://mapas.mma.gov.br/i3geo/datadownload.htm</a>.</p
Satellite scenes description, weather data at the satellite overpass time, and E<sub>TrF</sub> values.
<p>Satellite scenes description, weather data at the satellite overpass time, and E<sub>TrF</sub> values.</p
Slope, soil sampling points, and Compound Topographic Index (CTI) in the cerrado and pasture catchments.
<p>Slope, soil sampling points, and Compound Topographic Index (CTI) in the cerrado and pasture catchments.</p
Daily discharges and areal average rainfall for the cerrado and pasture catchments.
<p>Daily discharges and areal average rainfall for the cerrado and pasture catchments.</p