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 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.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

2002 ◽  
Vol 53 (1) ◽  
pp. 77 ◽  
Author(s):  
A. B. Potgieter ◽  
G. L. Hammer ◽  
D. Butler
Keyword(s):  
Spatial Patterns ◽  
Southern Oscillation ◽  
Wheat Yield ◽  
Temporal Patterns ◽  
Stress Index ◽  
Wheat Crop ◽  
Spatial And Temporal Variability ◽  
Spatial And Temporal Patterns ◽  
Yield Data ◽  
Australian Wheat

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Niño Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975–93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April–May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean–atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

A MOISTURE STRESS INDEX FOR WHEAT BY MEANS OF A MODULATED SOIL MOISTURE BUDGET

1968 ◽  
Vol 48 (5) ◽  
pp. 535-544 ◽  
Author(s):  
A. R. Mack ◽  
W. S. Ferguson
Keyword(s):  
Soil Moisture ◽  
Stress Function ◽  
Potential Evapotranspiration ◽  
Moisture Stress ◽  
Moisture Distribution ◽  
Stress Index ◽  
Wheat Crop ◽  
Moisture Budget ◽  
Dough Stage ◽  
Soil Moisture Budget

Actual evapotranspiration (AE), soil moisture distribution, and moisture stress for a wheat crop (PE-AE) were estimated by the modulated soil moisture budget of Holmes and Robertson. The estimated soil moisture was reasonably well correlated with soil moisture measured weekly by means of gypsum blocks. Wheat yields from experimental plots in the corresponding area were related more closely to the moisture stress function (PE-AE: r = − 0.83), than to the seasonal precipitation (r = 0.62), the potential evapotranspiration (PE) or the evapotranspiration ratio (AE/PE). Regression analyses showed that the grain yields were reduced by an average of 156 (±sb = 40) kg/ha per cm of moisture stress from emergence to harvest, or by 311 and 69 kg/ha per cm of stress, from the fifth-leaf to the soft-dough stage and from the soft-dough stage to maturity, respectively. The moisture stress function may be used to characterize the soil–plant–atmosphere environment for the growing season of a crop. Precipitation and evapotranspiration data are presented annually for three standardized growing periods at Brandon from 1921 to 1963.

scholarly journals Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171995 ◽  
Author(s):  
Tri-Long Nguyen ◽  
Géraldine Leguelinel-Blache ◽  
Jean-Marie Kinowski ◽  
Clarisse Roux-Marson ◽  
Marion Rougier ◽  
...  
Keyword(s):  
High Risk ◽  
Medication Safety ◽  
Multivariate Model ◽  
High Risk Patients ◽  
Model Based ◽  
Risk Patients

scholarly journals Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer
Keyword(s):  
High Resolution ◽  
Crop Production ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Model ◽  
Stress Index ◽  
Yield Prediction ◽  
Field Scale ◽  
Crop Water Stress Index ◽  
Sentinel 2

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.

scholarly journals Dietary Supplementation of Yerba Mate (Ilex paraguariensis) during the Dry Period Improves Redox Balance in Lactating Dairy Cows

Antioxidants ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 38 ◽  
Author(s):  
Olimpia Barbato ◽  
Belinda Holmes ◽  
Iulia-Elena Filipescu ◽  
Pietro Celi
Keyword(s):  
Milk Yield ◽  
Body Condition Score ◽  
Redox Balance ◽  
Stress Index ◽  
Ilex Paraguariensis ◽  
Yerba Mate ◽  
Dry Period ◽  
Yield Data ◽  
Holstein Friesian ◽  
Condition Score

Thirty-six pregnant Holstein–Friesian cows were used to study the effect of Yerba Mate (YM) supplementation during the dry period on redox balance. The treatments groups were Control (no YM), YM 250 (250 g/cow/day), and YM 500 (500 g/cow/day). Blood samples were obtained 30 days prepartum, at calving, and monthly postpartum until four months post calving. Liveweight (LW) and body condition score (BCS) were assessed prepartum, at calving, and then postpartum monthly until the end of the trial. Plasma was analyzed for hydroperoxides (d-ROMs), advanced oxidation protein products (AOPP), and biological antioxidant potential (BAP). The oxidative stress index (OSI) was calculated as OSI = ROMs/BAP × 100. Cows were milked twice daily, and milk yield data were recorded daily. Redox balance was improved by YM supplementation, as reflected in the lower OSI values observed in the YM groups. Yerba Mate supplementation significantly affected LW, but did not affect BCS. Milk yield averaged 28.1 ± 0.40, 29.0 ± 0.48, and 29.9 ± 0.46 L/cow/day in the Control, YM 250, and YM 500 groups, respectively, but was not significant. Nutritional manipulation during the dry period with Yerba Mate has demonstrated the potential to improve redox balance and milk yield.

Soil measurements and wheat yield response to superphosphate in Victoria

1975 ◽  
Vol 15 (72) ◽  
pp. 93
Author(s):  
B Palmer ◽  
VF McClelland ◽  
R Jardine
Keyword(s):  
Wheat Yield ◽  
Geographic Location ◽  
Yield Response ◽  
Soil Test ◽  
Soil Tests ◽  
Yield Data ◽  
Chemical Measurements ◽  
Soil Measurements ◽  
Yield Responses ◽  
Available Phosphate

The relationships between soil tests for 'plant available' phosphate and wheat yield response to applied superphosphate were examined and the extent to which these relationships were modified by other soil measurements was determined. Soil samples and wheat yield data were obtained from experiments conducted in the Victorian wheat belt. The sites were grouped into four relatively uniform classes using soil pH measurement and geographic location. The soil test values differed widely and were accountable for by the soil characteristics measured. However, the overall and within group yield responses to applied superphosphate could not be accounted for in terms of either the soil test value or the associated chemical measurements. By inference, yield response was clearly dependent on factors other than those determining the results of soil tests.

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