scholarly journals Use of Landsat images for yield evaluation within a small plot

2014 ◽  
Vol 60 (No. 11) ◽  
pp. 501-506 ◽  
Author(s):  
J. Kumhálová ◽  
F. Zemek ◽  
P. Novák ◽  
O. Brovkina ◽  
M. Mayerová
Keyword(s):  
Winter Wheat ◽  
Water Distribution ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Moisture Stress ◽  
Stress Index ◽  
Growth Stages ◽  
Landsat Images ◽  
Topographic Attributes ◽  
Small Plot

Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was –0.816 in the image acquisition date of 4. 6. 2011.

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

2010 ◽  
Vol 11 (4) ◽  
pp. 335-357 ◽  
Author(s):  
Fei Li ◽  
Yuxin Miao ◽  
Simon D. Hennig ◽  
Martin L. Gnyp ◽  
Xinping Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Different Growth Stages

scholarly journals Seasonal dynamics of lingonberry and blueberry spectra

Silva Fennica ◽  
2019 ◽  
Vol 53 (2) ◽  
Author(s):  
Petri Forsström ◽  
Jouni Peltoniemi ◽  
Miina Rautiainen
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
Leaf Growth ◽  
Vegetation Indices ◽  
Growing Season ◽  
Moisture Stress ◽  
Stress Index ◽  
Infrared Spectral

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350–2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Institute’s FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

Herbicides for Winter-hardy Wild Oat (Avena fatua) Control in Winter Wheat (Triticum aestivum)

1997 ◽  
Vol 11 (1) ◽  
pp. 35-38 ◽  
Author(s):  
Jeffrey A. Koscelny ◽  
Thomas F. Peeper
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Avena Fatua ◽  
Growth Stages ◽  
Wild Oat ◽  
Wheat Grain ◽  
Grain Yields

Diclofop at 840 g ai/ha, fenoxaprop at 90 g ai/ha, and imazamethabenz at 530 g ai/ha fall-applied controlled wild oat 96, 99, and 95% and increased wheat grain yields 26, 29, and 24%, respectively. These herbicides controlled wild oat over a wider range of growth stages than current labels indicate. The same treatments applied in March were less effective for wild oat control and did not increase wheat yield.

Effects of subsoil plastic film mulch on yield and water use of rainfed winter wheat

2018 ◽  
Vol 69 (12) ◽  
pp. 1197
Author(s):  
Zhang Mingming ◽  
Dong Baodi ◽  
Qiao Yunzhou ◽  
Yang Hong ◽  
Wang Yakai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Water Use Efficiency ◽  
Water Use ◽  
Crop Production ◽  
Wheat Yield ◽  
Limiting Factor ◽  
Growth Stages ◽  
Plastic Film ◽  
Use Efficiency

Water shortage is a limiting factor to crop production in North China. Mulching is a widely used approach to conserve soil water and improve crop yield. A 2-year field experiment was conducted at the Nanpi Eco-Agricultural Experimental Station of the Chinese Academy of Sciences in 2014–16, in which yields of winter wheat (Triticum aestivum L.) in a treatment with subsoil plastic film mulch were compared with non-mulch. The mulch treatment produced a 16.1% higher grain yield than the non-mulch treatment. The increase in grain yield was primarily due to a 10.1–10.9% increase in number of spikes per m2 and a 4.7–5.1% increase in number of grains per spike. Plants in the mulch treatment showed greater dry matter (DM) accumulation but similar harvest index. Yield improvement did not depend on increasing DM translocation, but was significantly related to DM accumulation at different growth stages. Increased DM accumulation before wintering, from jointing to heading and from anthesis to maturity, enhanced grain yield by promoting increased number of spikes and number of grains per spike. Soil evaporation was lower by 31.1% and transpiration increased by 28.0% in the mulch treatment, resulting in 8.9–9.4% higher water-use efficiency. Our results indicate that a subsoil plastic film mulch can effectively improve winter wheat yield and water-use efficiency under rain-fed conditions.

scholarly journals Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data

2009 ◽  
Vol 60 (1) ◽  
pp. 60 ◽  
Author(s):  
A. G. T. Schut ◽  
D. J. Stephens ◽  
R. G. H. Stovold ◽  
M. Adams ◽  
R. L. Craig
Keyword(s):  
Wheat Yield ◽  
Moisture Stress ◽  
Multivariate Model ◽  
Stress Index ◽  
Full Time ◽  
Yield Data ◽  
Modis Ndvi ◽  
Model Based ◽  
Production Forecasting ◽  
Forecasting System

The objective of this study was to improve the current wheat yield and production forecasting system for Western Australia on a LGA basis. PLS regression models including temporal NDVI data from AVHRR and/or MODIS, CR, and/or SI, calculated with the STIN, were developed. Census and survey wheat yield data from the Australian Bureau of Statistics were combined with questionnaire data to construct a full time-series for the years 1991–2005. The accuracy of fortnightly in-season forecasts was evaluated with a leave-year-out procedure from Week 32 onwards. The best model had a mean relative prediction error per LGA (RE) of 10% for yield and 15% for production, compared with RE of 13% for yield and 18% for production for the model based on SI only. For yield there was a decrease in RMSE from below 0.5 t/ha to below 0.3 t/ha in all years. The best multivariate model also had the added feature of being more robust than the model based on SI only, especially in drought years. In-season forecasts were accurate (RE of 10–12% and 15–18% for yield and production, respectively) from Week 34 onwards. Models including AVHRR and MODIS NDVI had comparable errors, providing means for predictions based on MODIS. It is concluded that the multivariate model is a major improvement over the current DAFWA wheat yield forecasting system, providing for accurate in-season wheat yield and production forecasts from the end of August onwards.

scholarly journals Dryland Winter Wheat Yield, Grain Protein, and Soil Nitrogen Responses to Fertilizer and Biosolids Applications

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Richard T. Koenig ◽  
Craig G. Cogger ◽  
Andy I. Bary
Keyword(s):  
Winter Wheat ◽  
Production Systems ◽  
Inorganic Nitrogen ◽  
Wheat Yield ◽  
Moisture Stress ◽  
Grain Protein Content ◽  
Grain Protein ◽  
Inorganic N ◽  
Winter Wheat Yield ◽  
Eastern Washington

Applications of biosolids were compared to inorganic nitrogen (N) fertilizer for two years at three locations in eastern Washington State, USA, with diverse rainfall and soft white, hard red, and hard white winter wheat (Triticum aestivumL.) cultivars. High rates of inorganic N tended to reduce yields, while grain protein responses to N rate were positive and linear for all wheat market classes. Biosolids produced 0 to 1400 kg ha−1(0 to 47%) higher grain yields than inorganic N. Wheat may have responded positively to nutrients other than N in the biosolids or to a metered N supply that limited vegetative growth and the potential for moisture stress-induced reductions in grain yield in these dryland production systems. Grain protein content with biosolids was either equal to or below grain protein with inorganic N, likely due to dilution of grain N from the higher yields achieved with biosolids. Results indicate the potential to improve dryland winter wheat yields with biosolids compared to inorganic N alone, but perhaps not to increase grain protein concentration of hard wheat when biosolids are applied immediately before planting.

scholarly journals Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12602
Author(s):  
Yu Feng ◽  
Wen Lin ◽  
Shaobo Yu ◽  
Aixia Ren ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Meteorological Factors ◽  
Wheat Yield ◽  
Control Variable ◽  
Northern China ◽  
Small Error ◽  
Soil Water Storage ◽  
Growth Stages ◽  
Yield Prediction ◽  
Fallow Period

In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

scholarly journals Analysis and comparison of vegetation indices of winter wheat crop areas, calculated on the basis of Sentinel-2 and fieldspec spectroradiometer data

2017 ◽  
pp. 37-46 ◽  
Author(s):  
Vadim Lyalko ◽  
Oleksii Sakhatsky ◽  
Galina Zholobak ◽  
Oksana Sybirtseva ◽  
Stanislav Dugin ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Wheat Crop ◽  
Growth Stages ◽  
Double Ratio ◽  
Winter Wheat Crop ◽  
Chlorophyll Index ◽  
Spectral Channels ◽  
Sentinel 2

Ten vegetation indices (VIs) were analyzed, which were calculated simultaneously based on Sentine-l2 data and on results of ground spectrometric survey by ASD FieldSpec® 3FR for the identically geographical sites of the production crops of winter wheat of two cultivars Bohdana and Skagen. The values of the most studied VIs on Sentinel-2 satellite data are similar by quantity to the same indices, calculated on the narrow spectral channels of ASD FieldSpec® 3FR, except for DRICI (Double ratio index for chlorophyll index) and СІ green (ratio green chlorophyll index), the satellite values of which are much lower than those received by spectroradiometer. It was shown that the differences of VIs received by Sentinel-2 and ASD FieldSpec® 3FR depend on the growth stages of winter wheat: during vegetation season the correlation coefficients between them increase for crop areas of both studied cultivars.

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