Spatial variability of maize leaf area and relationship between it and yield

2021 ◽  
Author(s):  
Lingling Zhang ◽  
Peng Yu ◽  
Jilong Liu ◽  
Qiang Fu ◽  
Junfeng Chen ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area ◽  
Maize Leaf

Nitrogen and Water Uptake on Sloping Lands of Thailand

2011 ◽  
Vol 356-360 ◽  
pp. 2484-2496
Author(s):  
Sireetorn Siriwong ◽  
Thanuchai Kongkaew ◽  
Georg Cadisch ◽  
Thomas Hilger
Keyword(s):  
Spatial Variability ◽  
Driving Force ◽  
N Fertilization ◽  
Control Measures ◽  
Yield Response ◽  
Maize Leaf ◽  
Mountainous Regions ◽  
N Concentration ◽  
Sloping Lands ◽  
Leaf N Concentration

Cultivation in the tropical mountainous regions by using hedgerow systems as erosion control measures is recommended due to its effectiveness in reducing soil erosion and in supplying N. However, competition for nutrients and water between crops and hedges reduces crop performance and yield response.13C stable isotope signature in combination with data on N and water availability and uptake in soil and plants framework was used to assess whether N or water function as a main driving force for spatial variability of crop yield along the alleys. The leaf δ13C values of maize were significantly (p<0.05) less depleted close to the hedges, suggesting that water stress was not the main driving force for spatial variability along the alleys. In the opposite, significant (p<0.05) N concentration depleted in maize leaf of plot with L. Leucocephala hedges, in particular at the row closed to hedgerow, in combination with significant (p<0.05) increase in δ13C values of leaf of the corresponding plot indicating the influence of N stress on poor maize performance and yield decrease. In addition, the significant (p<0.05) negative correlation between leaf δ13C values of maize, leaf N concentration and yield confirmed that N plays as a major role in crop decline towards hedgerows. Therefore, increasing amount of N fertilization to cropped area close to the hedgerows should be recommended for farmers, in order to encourage the acceptance of hedges system in tropical mountainous regions.

scholarly journals Sarpa salpa herbivory on shallow reaches of Posidonia oceanica beds

2014 ◽  
Vol 37 (1) ◽  
pp. 49-57 ◽  
Author(s):  
L. Steele ◽  
◽  
K. M. Darnelli ◽  
J. Cebrián ◽  
J. L. Sánchez-Lizaso ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area ◽  
Posidonia Oceanica ◽  
Sampling Time ◽  
Herbivorous Fish ◽  
Small Scale ◽  
Bite Size ◽  
Sarpa Salpa ◽  
Sampling Times

Here, we examined the temporal and small–scale spatial variability of grazing by the herbivorous fish Sarpa salpa on shallow beds of the temperate seagrass Posidonia oceanica. Herbivory intensity expressed as the percent of leaf area taken by fish bites was higher in September 2006 than in February 2007, and at 0.5 m than at 1.5 m during both sampling times. All S. salpa feeding at the shallow locations studied were juveniles, with bite sizes ranging from 0.03 to 0.62 cm2. Juveniles feeding at 1.5 m were larger in February 2007 than in September 2006, as evidenced by significant differences in mean bite size per shoot. However, the larger juveniles feeding at 1.5 m in February 2007 did not appear to feed as frequently as the comparatively smaller juveniles feeding at the same depth in September 2006, as suggested by significant differences in number of bites per shoot. The number of bites per shoot was also lower at 1.5 m than at 0.5 m in February 2007, although mean bite size did not differ significantly between the two depths at that sampling time. In general S. salpa juveniles did not select a particular range of leaf ages when feeding in the study locations, although the juveniles feeding at 1.5 m in September 2006 appeared to select mid–aged leaves. Fish did not show a preference for more epiphytized leaves. These results show that grazing activity by S. salpa juveniles in shallow reaches of P. oceanica meadows may vary temporally and across small changes in depth, which in turn may affect the overall intensity of herbivory on the seagrass.

scholarly journals Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1942 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mario Cunha
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Cost Functions ◽  
Support Vector ◽  
Area Index ◽  
Maize Leaf ◽  
Multispectral Data ◽  
Simple Ratio ◽  
Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

scholarly journals Spatial Variability of Leaf Area Index in Homogeneous Forests Relates to Local Variation in Tree Characteristics

2012 ◽  
Vol 58 (6) ◽  
pp. 633-640 ◽  
Author(s):  
Raphael Bequet ◽  
Matteo Campioli ◽  
Vincent Kint ◽  
Bart Muys ◽  
Jan Bogaert ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Local Variation ◽  
Area Index ◽  
Tree Characteristics

Temporal dynamics and spatial variability in the enhancement of canopy leaf area under elevated atmospheric CO2

2007 ◽  
Vol 13 (12) ◽  
pp. 2479-2497 ◽  
Author(s):  
HEATHER R. McCARTHY ◽  
RAM OREN ◽  
ADRIEN C. FINZI ◽  
DAVID S. ELLSWORTH ◽  
HYUN-SEOK KIM ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area ◽  
Temporal Dynamics ◽  
Atmospheric Co2 ◽  
Elevated Atmospheric Co2

scholarly journals Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2751
Author(s):  
Matthias Wengert ◽  
Hans-Peter Piepho ◽  
Thomas Astor ◽  
Rüdiger Graß ◽  
Jayan Wijesingha ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
Biomass Yield ◽  
Alley Cropping ◽  
High Spatial Resolution ◽  
Plant Traits ◽  
Agroforestry Systems ◽  
Area Index

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

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