scholarly journals A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time

2020 ◽  
Vol 86 ◽  
pp. 101988 ◽  
Author(s):  
Yetkin Özüm Durgun ◽  
Anne Gobin ◽  
Grégory Duveiller ◽  
Bernard Tychon
Keyword(s):  
Spatial Resolution ◽  
Wheat Yield ◽  
Yield Estimation ◽  
Calendar Time ◽  
Sampling Density ◽  
Temporal Sampling ◽  
Trade Offs

scholarly journals Leveraging eDNA to detect and monitor hybrid zones

2019 ◽  
Author(s):  
Kathryn Stewart ◽  
Scott A. Taylor
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Genomic Data ◽  
Environmental Dna ◽  
Hybrid Zones ◽  
Evolutionary Processes ◽  
Sampling Density ◽  
Temporal Sampling ◽  
Temporal And Spatial

Hybrid zones are important windows into evolutionary processes and our understanding of their significance and prevalence in nature has expanded quickly. Yet most hybridization research has restricted temporal and spatial resolution, limiting our ability to draw broad conclusions about evolutionary and conservation related outcomes. Here, we argue rapidly advancing environmental DNA (eDNA) methodology should be adopted for studies of hybrid zones to increase temporal sampling (contemporary and historical), to refine and geographically expand sampling density, and to collect data for taxa that are difficult to directly sample. Genomic data in the environment offer the potential for near real-time biological tracking and eDNA provides broad, as yet untapped potential to address eco-evolutionary questions.

Crop yield evaluation using sentinel satellite imagery and modelling methods

2020 ◽  
Author(s):  
Anne Gobin
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Wheat Yield ◽  
Crop Growth ◽  
Sensor Technology ◽  
Temporal Sampling ◽  
Climate Resilience ◽  
Trade Offs ◽  
Crop Development ◽  
Arable Crop

<p>Agricultural yield is largely determined by weather conditions during the crop growing season. A comparison of meteorological indicators between low and high arable yields revealed significant (p > 0.05) differences in meteorological indicators (Gobin, 2018), and these change with crop. Further analysis revealed differences in climate resilience (Kahiluoto et al., 2019).</p><p>An important aspect of crop yield assessment concerns crop growth development and subsequent yield prediction (Durgun et al., 2016). Current models have predominantly concentrated on the relation between meteorological data and crop yield (Gobin et al., 2017). A lot of data are available on the input side to include soil and weather, but very few on crop development and yield at the field scale.</p><p>A new era of satellite remote sensing and sensor technology has already offered a paradigm shift to data rich environments with unprecedented possibilities to monitor crop development at higher spatial, temporal and spectral resolutions. Combining modelling and statistical analysis with monitoring from remote sensing presents new opportunities to understand crop growth as a basis for crop yield assessment (Durgun et al., 2020) and further developments in the agriculture, insurance and bio-economy sector.</p><p>Examples of common arable crop growth assessment will be drawn from different grants and projects.</p><p>References:</p><ul><li>Durgun, Ö, Gobin, A., Duveillier, G., Tychon, B., 2020. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. International Journal of Applied Earth Observations and Geoinformation, 86. https://doi.org/10.1016/j.jag.2019.101988</li> <li>Durgun, Y.Ö., Gobin, A., Vandekerchove, R., Tychon, B., 2016. Crop Area Mapping using 100m PROBA-V time series. Remote Sensing 8(7), 585; www.doi.org/10.3390/rs8070585.</li> <li>Gobin, A., Kersebaum K.C., Eitzinger J., Trnka M., Hlavinka P., Takáč J., Kroes J., Ventrella D., Dalla Marta A., Deelstra J., Lalić B., Nejedlik P., Orlandini S., Peltonen-Sainio P., Rajala A., Saue T., Şaylan L., Stričevic R., Vučetić V., Zoumides C., 2017. Variability in the water footprint of arable crop production across European regions. Water 2017, 9(2), 93; https://doi.org/10.3390/w9020093</li> <li>Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009</li> <li>Kahiluoto H., Kaseva, J., Balek, J., Olesen, J.E., Ruiz-Ramos, M., Gobin, A., Kersebaum, K.C., Takáč, J., Ruget, F., Ferrise, R., Bezak, P., Capellades, G., Dibari, C., Mäkinen, H., Nendel, C., Ventrella, D., Rodríguez, A., Bindi, M., Trnka M., 2019. Decline in climate resilience of European wheat. Proceedings of the National Academy of Sciences of the USA 116: 123-128. https://doi.org/10.1073/pnas.1804387115</li> </ul>

scholarly journals Leveraging eDNA to detect and monitor hybrid zones

2019 ◽  
Author(s):  
Kathryn Stewart ◽  
Scott A. Taylor
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Genomic Data ◽  
Environmental Dna ◽  
Hybrid Zones ◽  
Evolutionary Processes ◽  
Sampling Density ◽  
Temporal Sampling ◽  
Temporal And Spatial

Hybrid zones are important windows into evolutionary processes and our understanding of their significance and prevalence in nature has expanded quickly. Yet most hybridization research has restricted temporal and spatial resolution, limiting our ability to draw broad conclusions about evolutionary and conservation related outcomes. Here, we argue rapidly advancing environmental DNA (eDNA) methodology should be adopted for studies of hybrid zones to increase temporal sampling (contemporary and historical), to refine and geographically expand sampling density, and to collect data for taxa that are difficult to directly sample. Genomic data in the environment offer the potential for near real-time biological tracking and eDNA provides broad, as yet untapped potential to address eco-evolutionary questions.

scholarly journals Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico

Agronomie ◽  
2004 ◽  
Vol 24 (6-7) ◽  
pp. 295-304 ◽  
Author(s):  
J. C. Rodriguez ◽  
B. Duchemin ◽  
R. Hadria ◽  
C. Watts ◽  
J. Garatuza ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wheat Yield ◽  
Yield Estimation ◽  
Yaqui Valley

scholarly journals Evolutionary and ecological perspectives on the wheat phenotype

2021 ◽  
Vol 288 (1958) ◽  
pp. 20211259
Author(s):  
Victor O. Sadras
Keyword(s):  
Precision Agriculture ◽  
Current Model ◽  
Crop Improvement ◽  
Wheat Yield ◽  
High Yield ◽  
Striking Similarity ◽  
Stabilizing Selection ◽  
Grain Number ◽  
Offspring Number ◽  
Trade Offs

Technologies, from molecular genetics to precision agriculture, are outpacing theory, which is becoming a bottleneck for crop improvement. Here, we outline theoretical insights on the wheat phenotype from the perspective of three evolutionary and ecologically important relations—mother–offspring, plant–insect and plant–plant. The correlation between yield and grain number has been misinterpreted as cause-and-effect; an evolutionary perspective shows a striking similarity between crop and fishes. Both respond to environmental variation through offspring number; seed and egg size are conserved. The offspring of annual plants and semelparous fishes, lacking parental care, are subject to mother–offspring conflict and stabilizing selection. Labile reserve carbohydrates do not fit the current model of wheat yield; they can stabilize grain size, but involve trade-offs with root growth and grain number, and are at best neutral for yield. Shifting the focus from the carbon balance to an ecological role, we suggest that labile carbohydrates may disrupt aphid osmoregulation, and thus contribute to wheat agronomic adaptation. The tight association between high yield and low competitive ability justifies the view of crop yield as a population attribute whereby the behaviour of the plant becomes subordinated within that of the population, with implications for genotyping, phenotyping and plant breeding.

Pulsatile influxes of H+, K+ and Ca2+ lag growth pulses of Lilium longiflorum pollen tubes

1999 ◽  
Vol 112 (10) ◽  
pp. 1497-1509
Author(s):  
M.A. Messerli ◽  
G. Danuser ◽  
K.R. Robinson
Keyword(s):  
Cell Surface ◽  
Spatial Resolution ◽  
Calcium Influx ◽  
Vision System ◽  
Sampling Rate ◽  
Lilium Longiflorum ◽  
Pollen Tubes ◽  
Phase Lag ◽  
Temporal Sampling ◽  
Steady Growth

Fluxes of H+, K+ and Ca2+ were measured with self-referencing ion-selective probes, near the plasma membrane of growing Lilium longiflorum pollen tubes. Measurements from three regions around short, steady-growing tubes showed small, steady influx of H+ over the distal 40 microm and a region of the tube within 50–100 microm of the grain with larger magnitude efflux from the grain. K+ fluxes were immeasurable in short tubes. Measurements of longer tubes that were growing in a pulsatile manner revealed a pulsatile influx of both H+ and K+ at the growing tip. The average fluxes at the cell surface during the peaks of the H+ and K+ pulses were 489+/-81 and 688+/-144 pmol cm-2 second-1, respectively. Growth was measured by tracking the pollen tips with a computer vision system that achieved a spatial resolution of approximately 1/10 pixel. The high spatial resolution enabled the detection of growth, and thus the changes in growth rates, with a temporal sampling rate of 1 frame/second. These data show that the H+ and K+ pulses have a phase lag of 103+/-9 and 100+/-11 degrees, respectively, with respect to the growth pulses. Calcium fluxes were also measured in growing tubes. During steady growth, the calcium influx was relatively steady. When pulsatile growth began, the basal Ca2+ influx decreased and a pulsatile component appeared, superimposed on the reduced basal Ca2+ flux. The peaks of the Ca2+ pulses at the cell surface averaged 38.4+/-2.5 pmol cm-2 second-1. Longer tubes had large pulsatile Ca2+ fluxes with smaller baseline fluxes. The Ca2+ influx pulses had a phase lag of 123+/-9 degrees with respect to the growth pulses.

Wheat yield estimation in northwest Iran

1981 ◽  
Vol 3 (4) ◽  
pp. 291-304 ◽  
Author(s):  
P.J. Slabbers ◽  
F.X. Dunin
Keyword(s):  
Wheat Yield ◽  
Yield Estimation ◽  
Northwest Iran
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